Show code
script src = "https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js"Show code
$(document).ready(function() {
$('body').prepend('<div class=\"zoomDiv\"><img src=\"\" class=\"zoomImg\"></div>');
// onClick function for all plots (img's)
$('img:not(.zoomImg)').click(function() {
$('.zoomImg').attr('src', $(this).attr('src')).css({width: '100%'});
$('.zoomDiv').css({opacity: '1', width: 'auto', border: '1px solid white', borderRadius: '5px', position: 'fixed', top: '50%', left: '50%', marginRight: '-50%', transform: 'translate(-50%, -50%)', boxShadow: '0px 0px 50px #888888', zIndex: '50', overflow: 'auto', maxHeight: '100%'});
});
// onClick function for zoomImg
$('img.zoomImg').click(function() {
$('.zoomDiv').css({opacity: '0', width: '0%'});
});
});
Show code
<script src="hideOutput.js"></script> Show code
$(document).ready(function() {
$chunks = $('.fold');
$chunks.each(function () { // add button to source code chunks
if ( $(this).hasClass('s') ) {
$('pre.r', this).prepend("<div class=\"showopt\">Show Source</div><br style=\"line-height:22px;\"/>");
$('pre.r', this).children('code').attr('class', 'folded');
} // add button to output chunks
if ( $(this).hasClass('o') ) {
$('pre:not(.r)', this).has('code').prepend("<div class=\"showopt\">Show Output</div><br style=\"line-height:22px;\"/>");
$('pre:not(.r)', this).children('code:not(r)').addClass('folded'); // add button to plots
$(this).find('img').wrap('<pre class=\"plot\"></pre>');
$('pre.plot', this).prepend("<div class=\"showopt\">Show Plot</div><br style=\"line-height:22px;\"/>");
$('pre.plot', this).children('img').addClass('folded');
}
}); // hide all chunks when document is loaded
$('.folded').css('display', 'none') // function to toggle the visibility
$('.showopt').click(function() {
var label = $(this).html();
if (label.indexOf("Show") >= 0) {
$(this).html(label.replace("Show", "Hide"));
} else {
$(this).html(label.replace("Hide", "Show"));
}
$(this).siblings('code, img').slideToggle('fast', 'swing');
});
}); Several issues were found in the first stage of the exploration of the matches.
Duplicated entries (relationships)
Cases that are victims and imputed at the same time
Unspecified dates
Originally imputed birth dates that must be corrected according to PO database
Define the a date of censorship
Show code
`%>%`<- magrittr::`%>%`
message(paste0("relationships with judicial proceedings ending after November 13, 2019\n n=",
format(as.numeric(table(Base_fiscalia_v2$termino_relacion_simple<"2019-11-13"))[1],big.mark=","),"; ",
scales::percent(as.numeric(prop.table(table(Base_fiscalia_v2$termino_relacion_simple<"2019-11-13"))), accuracy=0.1)[1]))
relationships with judicial proceedings ending after November 13, 2019
n=7,317; 1.3%Show code
cat("Information of P.O. using only relationships that ended before November 13, 2019")
Information of P.O. using only relationships that ended before November 13, 2019Show code
message(paste0('Original Prosecutors Office\n(n = ',format(nrow(subset(Base_fiscalia_v2, termino_relacion_simple<"2019-11-13")),big.mark=","),
';\nCauses= ',subset(Base_fiscalia_v2, termino_relacion_simple<"2019-11-13")%>% dplyr::distinct(ruc)%>% nrow() %>% format(big.mark=','),
';\nRel.=',subset(Base_fiscalia_v2, termino_relacion_simple<"2019-11-13")%>%dplyr::distinct(idrelacion)%>%nrow()%>%format(big.mark=','),
';\nRUC_Vic_Imp=',subset(Base_fiscalia_v2, termino_relacion_simple<"2019-11-13")%>%dplyr::mutate(rel=paste0(ruc,"_",idsujeto_victima,"_",idsujeto_imputado,"_","iddelito"))%>%dplyr::distinct(rel)%>%nrow()%>%format(big.mark=','),
';\nindividuals= ',subset(Base_fiscalia_v2, termino_relacion_simple<"2019-11-13")%>% dplyr::distinct(rut_enc_saf)%>% nrow() %>% format(big.mark=','),')'))
Original Prosecutors Office
(n = 560,959;
Causes= 488,613;
Rel.=560,952;
RUC_Vic_Imp=534,710;
individuals= 74,653)Show code
cat("Information of P.O. using only crimes committed before November 13, 2019")
Information of P.O. using only crimes committed before November 13, 2019Show code
message(paste0('Original Prosecutors Office\n(n = ',format(nrow(subset(Base_fiscalia_v2, fec_comision_simple<"2019-11-13")),big.mark=","),
';\nCauses= ',subset(Base_fiscalia_v2, fec_comision_simple<"2019-11-13")%>% dplyr::distinct(ruc)%>% nrow() %>% format(big.mark=','),
';\nRel.=',subset(Base_fiscalia_v2, fec_comision_simple<"2019-11-13")%>%dplyr::distinct(idrelacion)%>%nrow()%>%format(big.mark=','),
';\nRUC_Vic_Imp=',subset(Base_fiscalia_v2, fec_comision_simple<"2019-11-13")%>%dplyr::mutate(rel=paste0(ruc,"_",idsujeto_victima,"_",idsujeto_imputado,"_","iddelito"))%>%dplyr::distinct(rel)%>%nrow()%>%format(big.mark=','),
';\nindividuals= ',subset(Base_fiscalia_v2, fec_comision_simple<"2019-11-13")%>% dplyr::distinct(rut_enc_saf)%>% nrow() %>% format(big.mark=','),')'))
Original Prosecutors Office
(n = 566,843;
Causes= 493,586;
Rel.=566,836;
RUC_Vic_Imp=540,172;
individuals= 74,784)Show code
Entries with missing date of comission of crime: 240Show code
Entries with missing date of end of judicial proceedings: 21Show code
invisible("No puedo bajar Rooney Sans. Alternativas:")
font_add(family = "Rooney Sans", regular = paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path),"/_style/RooneySansRegular.otf"))
showtext_auto()
tab1_lab<- paste0('Original C1 Dataset \n(n = ', formatC(nrow(CONS_C1), format='f', big.mark=',', digits=0), ';\npatients: ',formatC(CONS_C1%>% dplyr::distinct(HASH_KEY)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab2_lab<- paste0('•Remove duplicated entries\\\\\\l•Overlapping treatments of patients\\\\\\l•Intermediate events of treatment (continuous referrals)\\\\\\l')
tab3_lab<- paste0(' C1 Dataset \n(n = ', formatC(nrow(CONS_C1_df_dup_SEP_2020), format='f', big.mark=',', digits=0), ';\npatients: ',formatC(CONS_C1_df_dup_SEP_2020%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab4_lab<- paste0('Original Prosecutors Office\n(n = ',format(nrow(Base_fiscalia_v2),big.mark=","),
';\nCauses= ',Base_fiscalia_v2%>% dplyr::distinct(ruc)%>% nrow() %>% format(big.mark=','),
';\nRel.=',Base_fiscalia_v2%>%dplyr::distinct(idrelacion)%>%nrow()%>%format(big.mark=','),
';\nRUC_Vic_Imp=',Base_fiscalia_v2%>%dplyr::mutate(rel=paste0(ruc,"_",idsujeto_victima,"_",idsujeto_imputado,"_","iddelito"))%>%dplyr::distinct(rel)%>%nrow()%>%format(big.mark=','),
';\nindividuals= ',Base_fiscalia_v2%>% dplyr::distinct(rut_enc_saf)%>% nrow() %>% format(big.mark=','),')')
tab5_lab<- paste0('•Filter crimes committed after study follow-up period\\\\\\l•Remove duplicated entries\\\\\\l•Long-to-wide relationships/crimes, end of judicial proceedings, penalty\\\\\\l•Correct dates (birth, comission of crime, end of judicial proceedings)\\\\\\l•Define cases that acted as victims & imputed in a cause\\\\\\l')
tab6_lab<- paste0("O.P. Dataset \n(n = ????;\n","","individuals= ",Base_fiscalia_v2%>% dplyr::distinct(rut_enc_saf)%>% nrow()%>% formatC(big.mark = ","),")")
#https://stackoverflow.com/questions/46750364/diagrammer-and-graphviz
#https://mikeyharper.uk/flowcharts-in-r-using-diagrammer/
#http://blog.nguyenvq.com/blog/2012/05/29/better-decision-tree-graphics-for-rpart-via-party-and-partykit/
#http://blog.nguyenvq.com/blog/2014/01/17/skeleton-to-create-fast-automatic-tree-diagrams-using-r-and-graphviz/
#https://cran.r-project.org/web/packages/DiagrammeR/vignettes/graphviz-mermaid.html
#https://stackoverflow.com/questions/39133058/how-to-use-graphviz-graphs-in-diagrammer-for-r
#https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781789802566/1/ch01lvl1sec21/creating-diagrams-via-the-diagrammer-package
#https://justlegal.be/2019/05/using-flowcharts-to-display-legal-procedures/
#
library(DiagrammeR) #⋉
plot_merge_flowchart<-
grViz([2452 chars quoted with '"'], width = 800,
height = 1200)
#https://stackoverflow.com/questions/1554635/graphviz-how-to-have-a-subgraph-be-left-to-right-when-main-graph-is-top-to-bot
#https://stackoverflow.com/questions/65509087/diagrammer-flowchart-align-vertical-nodes
#https://stackoverflow.com/questions/39451158/how-to-specify-vertical-alignment-of-nodes-in-r-package-diagrammer
#https://stackoverflow.com/questions/64323943/graphviz-and-dot-files-horizontal-and-vertical-node-alignment-intervening-node
#https://stackoverflow.com/questions/5424555/changing-edge-direction-in-dot
#https://graphviz.org/docs/attrs/rankdir/
#
Correct dates
Show code
plot_dob<-
Base_fiscalia_v2 %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(fec_nacimiento_simple)) %>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::summarise(n=n())%>%
dplyr::ungroup()%>%
dplyr::mutate(label_text=paste0(fec_nacimiento_qrt,"\nn= ",format(n,big.mark=","))) %>%
ggplot2::ggplot(aes(x = fec_nacimiento_qrt, y = n, label= label_text))+
geom_line()+
sjPlot::theme_sjplot2() +
scale_x_yearqtr(format="%YQ%q", n=18,
limits=c(zoo::as.yearqtr("1900-01-01"),
max=zoo::as.yearqtr("2019-11-13")))+
theme(legend.position="bottom")+
guides(fill=guide_legend(ncol=3))+
theme(legend.text = element_text(size=10))+
theme(legend.title = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank())+
theme(axis.text.x = element_text(vjust = 0.5,hjust = 0.5,angle = 60), plot.caption=element_text(hjust=0))
ggplotly(plot_dob, tooltip = c("label_text"))%>% layout(xaxis= list(showticklabels = T)) %>%
layout(autosize = T, height = 600*.7, width=800*.7) #margin=list( l = 50, r = 50, b = 100, t = 250, pad = 4))
From the Figure we can see that people in the first quarters of 2022 were much lower than the previous quarters. Also, much more people were born before 1940. Hence, many of the values beyond these dates could be the result of mistyping.
First, we look at the entries in which a crime was committed after the date of retrieval of the first database (November 13th, 2019) (n= 1,413, 0.2%). Additionally, we look at the cases in which a judiciary process was terminated after the date of retrieval (n= 7,108, 1.3%).
Show code
Base_fiscalia_v3 <-
Base_fiscalia_v2 %>%
dplyr::filter(fec_comision_simple<=as.Date("2019-11-13")) %>%
#dplyr::mutate(fec_nacimiento_simple= #dplyr::case_when(fec_nacimiento_simple<="1900-01-01"~as.Date(NA),T~fec_nacimiento_simple)) %>%
# dplyr::mutate(fec_nacimiento_simple= dplyr::case_when(rut_enc_saf=="fdc8ab217f201ce53f036d9f67aa4b9f"~"18-04-1969",
# "4d2e1d873e73d75d60b8a2682c5dfc00"
# "bf7539be5a2640954da0eb42ef8d217f"
# "e6526aa99b4c821dbd64987144728793"
# "f589934020facfb599e5958ae046694a"
dplyr::mutate(fec_comision_simple= dplyr::case_when(fec_comision_simple<="1900-01-01"~as.Date(NA),T~fec_comision_simple)) %>%
dplyr::mutate(termino_relacion_simple= dplyr::case_when(termino_relacion_simple<="1900-01-01"~as.Date(NA),T~termino_relacion_simple))
Check the consistency of SENDA and POs dates of birth. We arranged the PO database ordered by RUN and date of comission of the crime (from the oldest to the the most recent).
Show code
message(paste0("Patients with more than one date of birth in P.O. DB: ",
Base_fiscalia_v3 %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::mutate(n_fec=n_distinct(fec_nacimiento_simple)) %>%
dplyr::filter(n_fec>1) %>% nrow()))
fech_nac_fisc_vs_senda<-
Base_fiscalia_v3 %>%
#arrange the rut from the first date of comission of a crime, but we are not detecting if he/she is the victim or not
dplyr::arrange(rut_enc_saf, fec_comision_simple) %>%
dplyr::left_join(subset(CONS_C1_df_dup_SEP_2020, subset= dup==1,select=c("hash_key","fech_nac", "fech_ing", "edad_al_ing", "edad_ini_cons", "obs", "numero_de_hijos_mod", "escolaridad", "embarazo", "cat_ocupacional","estado_conyugal_2")),by=c("rut_enc_saf"="hash_key")) %>%
dplyr::mutate(fech_nac=as.Date(fech_nac)) %>%
dplyr::select(rut_enc_saf,encontrado_como_imputado,fec_nacimiento_simple,fec_comision_simple, gls_parentesco, termino_relacion_simple,fech_nac, fech_ing, edad_al_ing, edad_ini_cons,contains("rpa"), obs, numero_de_hijos_mod, escolaridad, embarazo, cat_ocupacional,estado_conyugal_2) %>%
#keep only cases with discrepancies
dplyr::filter(dplyr::case_when(fec_nacimiento_simple!=fech_nac~T,T~F)) %>%
dplyr::mutate(obs=dplyr::case_when(rut_enc_saf %in% unlist(C1_fech_nac_nas)~"KNN",
grepl("1.4.", obs)~"1.4.",grepl("1.5.", obs)~"1.5.",grepl("1.6.XX.", obs)~"1.6.xx",T~""))%>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::mutate(n_dis_rucs=n_distinct(ruc)) %>%
dplyr::mutate(n_rows_hash=n(), rank_hash=row_number()) %>%
#count the times a HASH has been processed as an imputed
dplyr::mutate(n_imputed=sum(encontrado_como_imputado=="SI")) %>%
#get the second date of the comission of the crime
dplyr::mutate(lead_imputed=lead(fec_comision_simple)) %>%
#89,809 rows
#2022-03-28, changed with the first time a user was imputed (if she/he had imputations), but
# the amount of imputed
#https://stackoverflow.com/questions/63794410/r-slicing-a-grouped-data-frame-conditional-on-a-column
dplyr::filter((n_imputed>0 & encontrado_como_imputado=="SI" & rank_hash== suppressWarnings(min(rank_hash[encontrado_como_imputado=="SI"])))| (n_imputed==0 & rank_hash== 1)) %>%
dplyr::ungroup() %>%
#if its invalid, we assume that SENDA data is right; if not, we analyze the difference
dplyr::mutate(diff=ifelse(fec_nacimiento_simple<="1900-01-01",NA,abs(fec_nacimiento_simple-fech_nac))) %>%
#89,809 rows, but 11,505 unique RUNs
dplyr::select(-rank_hash)
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
message(paste0("Must take note that ",
CONS_C1_df_dup_SEP_2020 %>%
dplyr::filter(hash_key %in% unlist(C1_fech_nac_nas)) %>%
dplyr::select(hash_key, obs) %>%
dplyr::filter(!grepl("1.6.XX.",obs)) %>% nrow() %>% format(big.mark=","),
" cases that were selected to KNN imputation had not an observation of 1.6.XX process"))
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
CONS_C1_res_edad<-
CONS_C1 %>%
dplyr::mutate(fech_nac=lubridate::parse_date_time(stringi::stri_sub(`Codigo.Identificación`,-8,-1),"dmY"),
fech_nac=as.Date(as.character(fech_nac)),
fech_nac_num=unclass(fech_nac))%>%
dplyr::group_by(HASH_KEY) %>%
dplyr::summarise(median_fech_nac= median(fech_nac_num, na.rm=T),
mean_fech_nac= mean(fech_nac_num, na.rm=T))%>%
dplyr::mutate_if(is.numeric,funs(round(.,2)))
TOP_edad<-
CONS_TOP%>%
dplyr::mutate(fech_nac=as.Date(`Fecha.Nacimiento`, format="%d/%m/%Y"),
fech_nac=unclass(fech_nac)) %>%
dplyr::group_by(HASH_KEY) %>%
dplyr::summarise(median_fech_nac_top= median(fech_nac, na.rm=T),
mean_fech_nac_top= mean(fech_nac, na.rm=T))%>%
dplyr::mutate_if(is.numeric,funs(round(.,2)))
fech_nac_fisc_vs_senda2<-
fech_nac_fisc_vs_senda %>%
dplyr::left_join(CONS_C1_res_edad, by=c("rut_enc_saf"="HASH_KEY")) %>%
dplyr::left_join(TOP_edad, by=c("rut_enc_saf"="HASH_KEY")) %>%
dplyr::mutate(fech_nacimiento_num=unclass(fec_nacimiento_simple)) %>%
dplyr::mutate(diff2_mdn=ifelse(fec_nacimiento_simple<="1900-01-01",NA,abs(fech_nacimiento_num-median_fech_nac))) %>%
dplyr::mutate(diff2_m=ifelse(fec_nacimiento_simple<="1900-01-01",NA,abs(fech_nacimiento_num-mean_fech_nac)))%>%
dplyr::mutate(diff3_mdn=ifelse(fec_nacimiento_simple<="1900-01-01",NA,abs(fech_nacimiento_num-median_fech_nac_top))) %>%
dplyr::mutate(diff3_m=ifelse(fec_nacimiento_simple<="1900-01-01",NA,abs(fech_nacimiento_num-mean_fech_nac_top)))
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
fech_nac_fisc_vs_senda2 %>%
ggplot(aes(x=diff))+
geom_histogram(aes(x=diff), bins=40)+
sjPlot::theme_sjplot()+
xlim(0,20000)+
ylim(c(0,1600))+
scale_x_continuous(breaks=seq(0,30000,by=365*3), labels=seq(0,30000,by=365*3)/365, limits=c(0,20000))+
labs(x="Abs. difference between date of birth in PO vs. SENDA", y="Count",caption=paste0("Note. Users with discrepancies are the ", scales::percent(nrow(fech_nac_fisc_vs_senda)/length(unique(Base_fiscalia_v3$rut_enc_saf)))," of the sample; Differences due to missing values were not listed (n=",as.numeric(table(is.na(fech_nac_fisc_vs_senda$diff))[2]),");\nMedian= ",median(fech_nac_fisc_vs_senda$diff,na.rm=T)," [",quantile(fech_nac_fisc_vs_senda$diff,.25,na.rm=T),", ",quantile(fech_nac_fisc_vs_senda$diff,.75,na.rm=T),"] days;vertical lines= 1st and 3rd quartiles"))+
stat_summary(aes(y = 1, xintercept = after_stat(x)), fun = quantile, fun.args = list(probs = c(0.25, 0.75)), geom = "vline", orientation = "y") +
theme(text=element_text(size=74)) +
theme(plot.caption = element_text(hjust = 0, lineheight = .3))
Figure 1: Absolute differences between SENDA and POs date of birth
Show code
Base_fiscalia_v3 %>%
dplyr::filter(rut_enc_saf %in% unlist(fech_nac_fisc_vs_senda2)) %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::mutate(n_dis_fec=n_distinct(fec_comision)) %>%
ggplot(aes(x=n_dis_fec)) +
geom_histogram(aes(x=n_dis_fec))+
labs( caption=paste0("Note. ",dplyr::filter(Base_fiscalia_v3,rut_enc_saf %in% unlist(fech_nac_fisc_vs_senda2)) %>% distinct(rut_enc_saf) %>% nrow() %>% format(big.mark=",")," patients, ", dplyr::filter(Base_fiscalia_v3,rut_enc_saf %in% unlist(fech_nac_fisc_vs_senda2)) %>% nrow() %>% format(big.mark=",")," entries; vertical lines= 1st and 3rd quartiles"), x="Number of distinct dates by patient")+
stat_summary(aes(y = 1, xintercept = after_stat(x)), fun = quantile, fun.args = list(probs = c(0.25, 0.75)), geom = "vline", orientation = "y") +
sjPlot::theme_sjplot()+
theme(text=element_text(size=74)) +
theme(plot.caption = element_text(hjust = 0, lineheight = 1))
Figure 2: SENDA patients with differences in the dates of birth of SENDA DB. Number of distinct dates of comission of crimes
Show code
Base_fiscalia_v3 %>%
dplyr::filter(rut_enc_saf %in% unlist(fech_nac_fisc_vs_senda2)) %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::mutate(n_dis_fec=n_distinct(fec_comision)) %>%
dplyr::ungroup() %>%
dplyr::summarise(n=n(), min=min(n_dis_fec, na.rm=T), p10= quantile(n_dis_fec,.1,na.rm=T),p25=quantile(n_dis_fec,.25,na.rm=T),p50=quantile(n_dis_fec,.5,na.rm=T),p75=quantile(n_dis_fec,.75,na.rm=T),p90=quantile(n_dis_fec,.9,na.rm=T), max=max(n_dis_fec,na.rm=T)) %>% knitr::kable("markdown")
| n | min | p10 | p25 | p50 | p75 | p90 | max |
|---|---|---|---|---|---|---|---|
| 89809 | 1 | 3 | 6 | 11 | 19 | 28 | 245 |
Show code
#CONS_TOP
Show code
Base_fiscalia_v3 %>%
dplyr::filter(rut_enc_saf %in% unlist(fech_nac_fisc_vs_senda2)) %>%
dplyr::mutate(years_crime=dplyr::case_when(grepl("SI",encontrado_como_imputado)~as.numeric(fec_comision_simple-fec_nacimiento_simple)/365.25, T~NA_real_)) %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::slice_min(years_crime) %>%
dplyr::ungroup() %>%
ggplot(aes(x=years_crime)) +
geom_histogram(aes(x=years_crime))+
labs(caption="Note. 11,511 patients, 90,037 entries", x="Comission of crime age")+
stat_summary(aes(y = 1, xintercept = after_stat(x)), fun = quantile, fun.args = list(probs = c(0.025, 0.975)), geom = "vline", orientation = "y") +
sjPlot::theme_sjplot()+
theme(text=element_text(size=74)) +
theme(plot.caption = element_text(hjust = 0, lineheight = 1))
Figure 3: SENDA patients with differences in the dates of birth of SENDA DB. Distribution of age at comission of crime
Show code
rbind(c("Minimum age at crime comission",
#see the minimum age at crime comission
Base_fiscalia_v3 %>%
dplyr::filter(rut_enc_saf %in% unlist(fech_nac_fisc_vs_senda2)) %>%
dplyr::mutate(years_crime=dplyr::case_when(grepl("SI",encontrado_como_imputado)~as.numeric(fec_comision_simple-fec_nacimiento_simple)/365.25, T~NA_real_)) %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::slice_min(years_crime) %>%
dplyr::ungroup() %>%
dplyr::summarise(n=n(), min=min(years_crime, na.rm=T), p10= quantile(years_crime,.1,na.rm=T),p25=quantile(years_crime,.25,na.rm=T),p50=quantile(years_crime,.5,na.rm=T),p75=quantile(years_crime,.75,na.rm=T),p90=quantile(years_crime,.9,na.rm=T), max=max(years_crime,na.rm=T))),
#see the second age at crime comission
c("Second minimum age at crime comission",
Base_fiscalia_v3 %>%
dplyr::filter(rut_enc_saf %in% unlist(fech_nac_fisc_vs_senda2)) %>%
dplyr::mutate(years_crime=dplyr::case_when(grepl("SI",encontrado_como_imputado)~as.numeric(fec_comision_simple-fec_nacimiento_simple)/365.25, T~NA_real_)) %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::slice_min(years_crime,n=2) %>%
dplyr::slice_max(years_crime,n=1) %>%
dplyr::ungroup() %>%
dplyr::summarise(n=n(), min=min(years_crime, na.rm=T), p10= quantile(years_crime,.1,na.rm=T),p25=quantile(years_crime,.25,na.rm=T),p50=quantile(years_crime,.5,na.rm=T),p75=quantile(years_crime,.75,na.rm=T),p90=quantile(years_crime,.9,na.rm=T), max=max(years_crime,na.rm=T))))
n min p10
[1,] "Minimum age at crime comission" 11063 -4.375086 19.44695
[2,] "Second minimum age at crime comission" 11999 -4.375086 20.59685
p25 p50 p75 p90 max
[1,] 24.52977 32.93908 46.34771 110.2801 159.1211
[2,] 25.69199 34.09719 47.47707 111.3687 159.5483Show code
paste0("Patients w/ discrepancies that only have been victims: ",
Base_fiscalia_v3 %>%
dplyr::filter(rut_enc_saf %in% unlist(fech_nac_fisc_vs_senda2)) %>%
dplyr::mutate(years_crime=dplyr::case_when(grepl("SI",encontrado_como_imputado)~as.numeric(fec_comision_simple-fec_nacimiento_simple)/365.25, T~NA_real_)) %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::summarise(n_na=length(years_crime[is.na(years_crime)]),n_not_na=length(years_crime[!is.na(years_crime)])) %>%
dplyr::filter(n_not_na==0) %>% nrow() %>% format(big.mark=","))
[1] "Patients w/ discrepancies that only have been victims: 1,708"- See whether PO data was a mistype or invalid for other reasons (e.g., invalid age)
- Replace with PO in case age was imputed by the research team
- Define a tolerance value
Show code
#C1_fech_nac_nas
#1.4. 1.5.1.6.xx
invisible("Ver cómo impacta a las edades imputadas como el edad de inicio de consumo y esas cosas"))
#1.4. Replaced invalid ages with same users within the dataset
#1.7.04. Invalid Age Of Onset of Primary Substance, Higher than age
#1.7.05. Invalid Age Of Onset of Drug Use
#3.01.0b.HASH w/ more than one distinct Age of Onset of Drug Use.Minor differences within users,replaced with mean
#3.02.0b.HASH w/ more than one distinct Age of Onset of Drug Use Prim Subs. Minor differences within users,replaced with mea
#3.02.3.HASH w/ more than one distinct Age of Onset of Drug Use Prim Subs.Other;
#3.03.3.XX.HASH w/ more than one distinct Age of Onset Drug Use Primary Substance.Neural network imputation of ties
#1.6.XX.2.Age at admission less 18 years & had another date of birth that could be replaced
# ;1.5. Replaced invalid ages with TOP information
#1.6.02.0b;1.7.04. Invalid Age Of Onset of Primary Substance, Higher than age;
#
#1.6.03.1a;1.7.03. Invalid Age Of Onset of Drug Use, Higher than age;
#1.6.03.1a;1.7.04. Invalid Age Of Onset of Primary Substance, Higher than age;2
#1.6.03.1a;1.7.05. Invalid Age Of Onset of Drug Use, < 5 yrs age;
#1.6.05.2b1;1.7.06. Invalid Age Of Onset of Primary Substance, < 5 yrs age
#1.6.XX.2.Age at admission less 18 years & had another date of birth that could be replaced
#1.7.03. Invalid Age Of Onset of Drug Use, Higher than age
#1.7.04. Invalid Age Of Onset of Primary Substance, Higher than age
#1.7.05. Invalid Age Of Onset of Drug Use, < 5 yrs age
#1.7.06. Invalid Age Of Onset of Primary Substance, < 5 yrs age
#C1_fech_nac_nas
Show code
runs_that_share_birth_date_w_po<-
Base_fiscalia_v3 %>%
#arrange the rut from the first date of comission of a crime, but we are not detecting if he/she is the victim or not
dplyr::arrange(rut_enc_saf, fec_comision_simple) %>%
dplyr::left_join(subset(CONS_C1_df_dup_SEP_2020, subset= dup==1,select=c("hash_key","fech_nac", "fech_ing", "edad_al_ing", "edad_ini_cons", "obs", "numero_de_hijos_mod", "escolaridad", "embarazo", "cat_ocupacional","estado_conyugal_2")),by=c("rut_enc_saf"="hash_key")) %>%
dplyr::mutate(fech_nac=as.Date(fech_nac)) %>%
dplyr::select(rut_enc_saf,encontrado_como_imputado,fec_nacimiento_simple,fec_comision_simple, gls_parentesco, termino_relacion_simple,fech_nac, fech_ing, edad_al_ing, edad_ini_cons,contains("rpa"), obs, numero_de_hijos_mod, escolaridad, embarazo, cat_ocupacional,estado_conyugal_2) %>%
#keep only cases with discrepancies
dplyr::filter(dplyr::case_when(fec_nacimiento_simple==fech_nac~T,T~F)) %>%
dplyr::distinct(rut_enc_saf)
paste0("Admission age in SENDA database, of patients with the same birth date with PO")
[1] "Admission age in SENDA database, of patients with the same birth date with PO"Show code
summ_adm_age_perc<-
CONS_C1_df_dup_SEP_2020 %>%
dplyr::filter(hash_key %in% unlist(runs_that_share_birth_date_w_po)) %>%
dplyr::group_by(hash_key) %>%
dplyr::slice_min(edad_al_ing,n=1) %>%
ungroup() %>%
dplyr::summarise(n=n(), p2.5= quantile(edad_al_ing,.025,na.rm=T),p97.5=quantile(edad_al_ing,.975,na.rm=T))
summ_adm_age_perc
# A tibble: 1 x 3
n p2.5 p97.5
<int> <dbl> <dbl>
1 62752 20.3 58.8Show code
# 62752 20.3 58.8
paste0("To get the age of the comission: as.numeric(as.Date('2019-11-13')-as.Date('2005-11-13'))/365.25 = ")
[1] "To get the age of the comission: as.numeric(as.Date('2019-11-13')-as.Date('2005-11-13'))/365.25 = "Show code
paste0(as.numeric(as.Date('2019-11-13')-as.Date('2005-11-13'))/365.25)
[1] "13.9986310746064"Show code
paste0(" years. ut this date may depend on the date of the comission of the crime")
[1] " years. ut this date may depend on the date of the comission of the crime"Show code
paste0("Age of crime comission in PO database, of patients with the same birth date of PO")
[1] "Age of crime comission in PO database, of patients with the same birth date of PO"Show code
summ_years_crime_perc<-
Base_fiscalia_v3 %>%
dplyr::filter(rut_enc_saf %in% unlist(runs_that_share_birth_date_w_po)) %>%
#filter people that committed a crime (excluiding those that were victims)
dplyr::mutate(years_crime=dplyr::case_when(grepl("SI",encontrado_como_imputado)~as.numeric(fec_comision_simple-fec_nacimiento_simple)/365.25, T~NA_real_)) %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::slice_min(years_crime,n=1) %>%
dplyr::ungroup() %>%
dplyr::summarise(n=n(), p2.5= quantile(years_crime,.025,na.rm=T),p97.5=quantile(years_crime,.975,na.rm=T))
summ_years_crime_perc
# A tibble: 1 x 3
n p2.5 p97.5
<int> <dbl> <dbl>
1 60130 16.2 55.1Show code
# 60108 16.1 55.0
paste0("Patients with one record with the same date of birth of PO in the original TOP database")
[1] "Patients with one record with the same date of birth of PO in the original TOP database"Show code
runs_keepPO_dates_TOP<-
fech_nac_fisc_vs_senda2 %>%
#the original birth date of SENDA or TOP might match with the PO birth date
dplyr::left_join(dplyr::mutate(CONS_TOP[, c("HASH_KEY","Fecha.Nacimiento")], fech_nac2=as.Date(`Fecha.Nacimiento`, format="%d/%m/%Y")), by=c("rut_enc_saf"="HASH_KEY","fec_nacimiento_simple"="fech_nac2")) %>%
dplyr::filter(!is.na(Fecha.Nacimiento)) %>%
dplyr::distinct(rut_enc_saf) %>% unlist() %>% as.character()
format(length(runs_keepPO_dates_TOP),big.mark=",")
[1] "646"Show code
paste0("Patients with one record with the same date of birth of PO in the original C1 database")
[1] "Patients with one record with the same date of birth of PO in the original C1 database"Show code
runs_keepPO_dates_C1<-
fech_nac_fisc_vs_senda2 %>%
#the original birth date of SENDA or TOP might match with the PO birth date
dplyr::left_join(dplyr::mutate(CONS_C1[,c("HASH_KEY","Codigo.Identificación")],fech_nac2=as.Date(as.character(lubridate::parse_date_time(stringi::stri_sub(`Codigo.Identificación`,-8,-1),"dmY")))), by=c("rut_enc_saf"="HASH_KEY","fec_nacimiento_simple"="fech_nac2")) %>%
dplyr::filter(!is.na(`Codigo.Identificación`)) %>%
dplyr::distinct(rut_enc_saf) %>% unlist() %>% as.character()
format(length(runs_keepPO_dates_C1),big.mark=",")
[1] "1,636"Show code
message(paste0("Patients with at least one record with the same date birth of PO in the original C1 or TOP database: ",format(length(unique(c(runs_keepPO_dates_TOP, runs_keepPO_dates_C1))),big.mark=",")))
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
flow_fech_nac_fisc_vs_senda <-
fech_nac_fisc_vs_senda2 %>% #fech_nac_fisc_vs_senda
#years crime commited with the first and second minimum date of comission of the crime
dplyr::mutate(years_crime_po=as.numeric(fec_comision_simple-fec_nacimiento_simple)/365.25,
years_crime_po2nd=as.numeric(lead_imputed-fec_nacimiento_simple)/365.25,
years_crime_senda=as.numeric(fec_comision_simple-fech_nac)/365.25) %>%
#format birth dates of TOP and C1 original diverging dates into a single one
dplyr::mutate(across(c("median_fech_nac","mean_fech_nac","median_fech_nac_top","mean_fech_nac_top"),~as.Date.numeric(.,origin="1970-01-01"))) %>%
#admission age according to POs birth date
dplyr::mutate(po_edad_al_ing=as.numeric(fech_ing-fec_nacimiento_simple)/365.25) %>%
dplyr::relocate(po_edad_al_ing, .after=edad_al_ing)%>%
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#Invalid dates
dplyr::mutate(c_22_1_a0ab= dplyr::case_when(is.na(diff)~1,T~0)) %>%
#Differences of birth dates below 3 months?
dplyr::mutate(c_22_1_a0cd= dplyr::case_when(diff<90~1,T~0)) %>%
#at least one record of the original TOP or C1 database matched w/ birth date of PO?
dplyr::mutate(c_22_1_a0ef= dplyr::case_when(rut_enc_saf %in% unique(c(runs_keepPO_dates_TOP, runs_keepPO_dates_C1))~1,T~0)) %>%
#replaced previously in SENDA DB
dplyr::mutate(c_22_1_a12= dplyr::case_when(nchar(obs)>1~1,T~0)) %>%
#Committed a crime?
dplyr::mutate(c_22_1_a_12_ab= dplyr::case_when(n_imputed>0~1,T~0)) %>%
#Aberrant admission or crime ages (%tiles 2.5 | 97.5)
#if age at crime is rare but SENDA is not, keep date of birth of SENDA
dplyr::mutate(c_22_1_a1a_12_a= dplyr::case_when(years_crime_po<round(as.numeric(summ_years_crime_perc[2]),2) & years_crime_senda>=round(as.numeric(summ_years_crime_perc[2]),2)~"keep senda (yrs crime), PO much lower than expected",
years_crime_po>round(as.numeric(summ_years_crime_perc[3]),2) & years_crime_senda<=round(as.numeric(summ_years_crime_perc[3]),2)~"keep senda (yrs crime), PO much larger than expected",
T~"")) %>%
#if admission age is rare but SENDA is not, keep date of birth of SENDA
dplyr::mutate(c_22_1_a1a_12_b= dplyr::case_when(po_edad_al_ing<round(as.numeric(summ_adm_age_perc[2]),2) & edad_al_ing>=round(as.numeric(summ_adm_age_perc[2]),2)~"keep senda (adm age), PO much lower than expected",
years_crime_po>round(as.numeric(summ_adm_age_perc[3]),2) & edad_al_ing<=round(as.numeric(summ_adm_age_perc[3]),2)~"keep senda (adm age), PO much larger than expected",
T~"")) %>%
dplyr::mutate(c_22_1_a1a_12_a2= dplyr::case_when(years_crime_senda<round(as.numeric(summ_years_crime_perc[2]),2) & years_crime_po>=round(as.numeric(summ_years_crime_perc[2]),2)~"keep po (yrs crime), SENDA much lower than expected",
years_crime_senda>round(as.numeric(summ_years_crime_perc[3]),2) & years_crime_po<=round(as.numeric(summ_years_crime_perc[3]),2)~"keep po (yrs crime), SENDA much larger than expected",
T~"")) %>%
dplyr::mutate(c_22_1_a1a_12_b2= dplyr::case_when(edad_al_ing<round(as.numeric(summ_adm_age_perc[2]),2) & po_edad_al_ing>=round(as.numeric(summ_adm_age_perc[2]),2)~"keep po (adm age), SENDA much lower than expected",
edad_al_ing>round(as.numeric(summ_adm_age_perc[3]),2) & years_crime_po<=round(as.numeric(summ_adm_age_perc[3]),2)~"keep po (adm age), SENDA much larger than expected",
T~"")) %>%
dplyr::mutate(c_22_1_a1a_1234= dplyr::case_when(nchar(c_22_1_a1a_12_a)>2 & nchar(c_22_1_a1a_12_b)>2 & nchar(c_22_1_a1a_12_a2)>2 & nchar(c_22_1_a1a_12_b2)>2~"Keep both", nchar(c_22_1_a1a_12_a)>2 & nchar(c_22_1_a1a_12_b)>2~ "Keep SENDA",nchar(c_22_1_a1a_12_a2)>2 & nchar(c_22_1_a1a_12_b2)>2~ "Keep PO",
T~"")) %>%
#age at admission of PO is in conflict with senda age of starting using
dplyr::mutate(c_22_1_a1a_1234_ab= dplyr::case_when(po_edad_al_ing<edad_ini_cons~"admitted before started using", T~"")) %>%
dplyr::mutate(c_22_1_a2a_4b_12= dplyr::case_when(!is.na(clasificacion_penarpa_1_49)&years_crime_senda>18~"SENDA age inconsistent w/ Juvenile Criminal Responsibility Law", T~""))
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#EXPORT_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
flow_fech_nac_fisc_vs_senda %>%
#do not include invalid dates, differences of less than 90 days and imputed previously
dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a12==0) %>% data.frame() %>%
write.table(file= paste0(path,"/dates_po_senda.txt"), dec=",", row.names=F)
#flow_fech_nac_fisc_vs_senda %>%
#do not include invalid dates, differences of less than 90 days and cases imputed previously
#dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a12==0) %>%
#found as an imputed
#dplyr::filter(c_22_1_a_12_ab==0) %>% #639, have not been imputed
#dplyr::filter(c_22_1_a_12_ab==1) %>%
#dplyr::filter(c_22_1_a1a_12_a!=""|c_22_1_a1a_12_b!="") %>% nrow()#294 have at least one aberrant case in PO
#dplyr::filter(c_22_1_a1a_12_a=="",c_22_1_a1a_12_b=="") %>% nrow()#3241 dont have any aberrant cases in PO
#dplyr::filter(c_22_1_a1a_12_a2!=""|c_22_1_a1a_12_b2!="") %>% nrow()#392 have at least one aberrant case in SENDA
#dplyr::filter(c_22_1_a1a_12_a2=="",c_22_1_a1a_12_b2=="") %>% nrow()#3143 dont have any aberrant cases in SENDA
#dplyr::filter(c_22_1_a1a_12_a=="",c_22_1_a1a_12_b=="",
# c_22_1_a1a_12_a2=="",c_22_1_a1a_12_b2=="") %>% nrow()#2866 dont have any aberrant cases in SENDA or PO
Show code
paste0("Paired SENDA patients with POs\n(22_1)\n(p= ", format(length(unique(Base_fiscalia_v3$rut_enc_saf)),big.mark=","),";n= ", format(nrow(Base_fiscalia_v3),big.mark=","),"; ",scales::percent(length(unique(Base_fiscalia_v3$rut_enc_saf))/length(unique(CONS_C1_df_dup_SEP_2020$hash_key))),")")
paste0("Paired w/ discrepancies \n(22_1_a)\n(p= ",format(nrow(fech_nac_fisc_vs_senda2),big.mark=","),";\n",
scales::percent(nrow(fech_nac_fisc_vs_senda2)/length(unique(Base_fiscalia_v3$rut_enc_saf))),";\nn=",
format(nrow(dplyr::filter(Base_fiscalia_v3, rut_enc_saf %in% fech_nac_fisc_vs_senda2$rut_enc_saf)), big.mark=","),
"; ",scales::percent(nrow(fech_nac_fisc_vs_senda2)/length(unique(CONS_C1_df_dup_SEP_2020$hash_key))),")")
paste0("Paired w/o discrepancies \n(22_1_b)\n(p= ",format(length(unique(Base_fiscalia_v3$rut_enc_saf)[!unique(Base_fiscalia_v3$rut_enc_saf) %in% fech_nac_fisc_vs_senda2$rut_enc_saf]),big.mark=","),";\nn=",
format(nrow(dplyr::filter(Base_fiscalia_v3, !rut_enc_saf %in% fech_nac_fisc_vs_senda2$rut_enc_saf)), big.mark=","),"; ",
scales::percent(length(unique(Base_fiscalia_v3$rut_enc_saf)[!unique(Base_fiscalia_v3$rut_enc_saf) %in% fech_nac_fisc_vs_senda2$rut_enc_saf])/length(unique(Base_fiscalia_v3$rut_enc_saf))),";\nn=",
scales::percent(length(unique(Base_fiscalia_v3$rut_enc_saf)[!unique(Base_fiscalia_v3$rut_enc_saf) %in% fech_nac_fisc_vs_senda2$rut_enc_saf])/length(unique(CONS_C1_df_dup_SEP_2020$hash_key))),")")
#65042-1774
paste0("Invalid dates\n(22_1_a0a)\n(p= ",format(as.numeric(table(is.na(fech_nac_fisc_vs_senda2$diff))[2]),big.mark=","),";\nn= ", format(nrow(dplyr::filter(Base_fiscalia_v3, rut_enc_saf %in% fech_nac_fisc_vs_senda2$rut_enc_saf[is.na(fech_nac_fisc_vs_senda2$diff)])),big.mark=","),";\n",scales::percent(as.numeric(table(is.na(fech_nac_fisc_vs_senda2$diff))[2])/length(unique(Base_fiscalia_v3$rut_enc_saf))),"; ",scales::percent(as.numeric(table(is.na(fech_nac_fisc_vs_senda2$diff))[2])/length(unique(CONS_C1_df_dup_SEP_2020$hash_key))),")")
paste0("<90 days differences\n(22_1_a0c)\n(p= ",format(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==1) %>% nrow(),big.mark=","),";\nn= ",format(nrow(dplyr::filter(Base_fiscalia_v3, rut_enc_saf %in% flow_fech_nac_fisc_vs_senda$rut_enc_saf[flow_fech_nac_fisc_vs_senda$c_22_1_a0cd==1])),big.mark=","),"; ",scales::percent(as.numeric(table(flow_fech_nac_fisc_vs_senda$c_22_1_a0cd)[2])/length(unique(Base_fiscalia_v3$rut_enc_saf))),"; ",scales::percent(as.numeric(table(flow_fech_nac_fisc_vs_senda$c_22_1_a0cd)[2])/length(unique(CONS_C1_df_dup_SEP_2020$hash_key))),")")
paste0("Differences >=90d_22_1_a0d\n(n= ",format(flow_fech_nac_fisc_vs_senda%>%dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0)%>%nrow(),big.mark=","),";\n",scales::percent(flow_fech_nac_fisc_vs_senda%>%dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0)%>%nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf))),"; ",scales::percent(flow_fech_nac_fisc_vs_senda%>%dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0)%>%nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key))),")")
paste0("Original C1 or TOP record did not matched age (22_1_a0ef)\n(n= ",format(flow_fech_nac_fisc_vs_senda%>%dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0)%>%nrow(),big.mark=","),";\n",scales::percent(flow_fech_nac_fisc_vs_senda%>%dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0)%>%nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)),accuracy=0.1),"; ",scales::percent(flow_fech_nac_fisc_vs_senda%>%dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0)%>%nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)),accuracy=0.1),")")
paste0("Original C1 or TOP record matched age (22_1_a0ef)\n(n= ",format(flow_fech_nac_fisc_vs_senda%>%dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==1)%>%nrow(),big.mark=","),";\n", format(nrow(dplyr::filter(Base_fiscalia_v3, rut_enc_saf %in% as.character(unlist(flow_fech_nac_fisc_vs_senda%>%dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==1)%>%dplyr::select(rut_enc_saf))))),big.mark=","),";\n",scales::percent(flow_fech_nac_fisc_vs_senda%>%dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==1)%>%nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)),accuracy=0.1),"; ",scales::percent(flow_fech_nac_fisc_vs_senda%>%dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==1)%>%nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)),accuracy=0.1),")")
paste0("(22_1_a1)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a1a)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
#keep senda= c_22_1_a1a_12_a (yrs_crime) c_22_1_a1a_12_b (adm_age) keep po= c_22_1_a1a_12_a2 c_22_1_a1a_12_b2
paste0("(22_1_a1a_1)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1, grepl("both",c_22_1_a1a_1234)) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,grepl("both",c_22_1_a1a_1234)) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,grepl("both",c_22_1_a1a_1234)) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a1a_2)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1, grepl("PO",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,grepl("PO",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,grepl("PO",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a1a_3)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1, grepl("SENDA",c_22_1_a1a_1234)) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,grepl("SENDA",c_22_1_a1a_1234)) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,grepl("SENDA",c_22_1_a1a_1234)) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a1a_4)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a1a_4a)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)>2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a1a_4b)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a1b)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==0) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==0) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==0) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a1b_a)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)>2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a1b_b)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==1,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
invisible("mirar caso d36b9cd8f1aa0a07ad209b0006af1465, hace ruido por RPA pero edades muy bajas en edad SENDA")
paste0("(22_1_a2)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2a)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2a_1)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("both",c_22_1_a1a_1234)) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("both",c_22_1_a1a_1234)) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("both",c_22_1_a1a_1234)) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2a_1a)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("both",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)>2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("both",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("both",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2a_1b)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("both",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("both",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("both",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2a_2)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("PO",c_22_1_a1a_1234)) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("PO",c_22_1_a1a_1234)) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("PO",c_22_1_a1a_1234)) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2a_2a)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("PO",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)>2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("PO",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("PO",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2a_2b)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("PO",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("PO",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("PO",c_22_1_a1a_1234),nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2a_3)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("SENDA",c_22_1_a1a_1234)) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("SENDA",c_22_1_a1a_1234)) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, grepl("SENDA",c_22_1_a1a_1234)) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("RPA cases involved in Rare admission & crime ages: ")
flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)>2) %>% janitor::tabyl(clasificacion_penarpa_1_49)
paste0("(22_1_a2a_4)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("RPA cases involved in Rare admission & crime ages: ")
flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2) %>% janitor::tabyl(clasificacion_penarpa_1_49)
if(no_mostrar==0){
flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2) %>% dplyr::filter(!is.na(clasificacion_penarpa_1_49)) %>% dplyr::select(rut_enc_saf, fec_nacimiento_simple, fech_nac, fech_ing, edad_al_ing, po_edad_al_ing, years_crime_po, years_crime_senda, contains("c_22_1_")) %>% View()
}
paste0("(22_1_a2a_4a)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)>2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2a_4b)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2a_4b_1)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2, nchar(c_22_1_a2a_4b_12)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2,nchar(c_22_1_a2a_4b_12)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2,nchar(c_22_1_a2a_4b_12)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
#2947, antes era 2949, despues de que declaré a los missing se restaron?
paste0("(22_1_a2a_4b_2)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2,nchar(c_22_1_a2a_4b_12)>2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2,nchar(c_22_1_a2a_4b_12)>2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1,nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2,nchar(c_22_1_a2a_4b_12)>2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2b)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",
scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2b_a)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)>2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)>2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2b_b)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2b_b_1)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2, nchar(c_22_1_a2a_4b_12)<2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2, nchar(c_22_1_a2a_4b_12)<2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2, nchar(c_22_1_a2a_4b_12)<2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
paste0("(22_1_a2b_b_2)\n(n= ", flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2, nchar(c_22_1_a2a_4b_12)>2) %>% nrow(),";\n", scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2, nchar(c_22_1_a2a_4b_12)>2) %>% nrow()/length(unique(Base_fiscalia_v3$rut_enc_saf)), accuracy=0.1),"; ",scales::percent(flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2, nchar(c_22_1_a2a_4b_12)>2) %>% nrow()/length(unique(CONS_C1_df_dup_SEP_2020$hash_key)), accuracy=0.1),")")
Show code
#knitr::include_graphics(paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path), "/_figs/diagram_age_differences.svg"))
tiger <- image_read_svg(paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path), "/_figs/diagram_age_differences.svg"))#, width = 350
# image_fill(tiger,
# color = "white",
# refcolor = "transparent",
# fuzz = 4,
# point = "+1+1" # start at top left 1 pixel in
# )
bpmn_file0 <- system.file(paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path), "/_figs/diagram_age_differences.bpmn"), package = "bpmn")
bpmn::bpmn(paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path), "/_figs/diagram_age_differences.bpmn"))
Apply rules
Show code
# (22_1_a1a_1)= Keep PO
# (22_1_a1a_2)= Keep PO
# (22_1_a1a_3)= Keep SENDA
# (22_1_a1a_4a)= Keep SENDA
# (22_1_a1a_4b)= Keep PO
# (22_1_a1b_a)=Keep SENDA
# (22_1_a1b_b)= Keep PO
# (22_1_a2a_1a)=Keep SENDA
# (22_1_a2a_1b)= Keep PO
# (22_1_a2a_2a)= Keep SENDA
# (22_1_a2a_2b)= Keep PO
# (22_1_a2a_3)= Keep SENDA
# (22_1_a2a_4a)= Keep SENDA
# (22_1_a2a_4b_2)= Keep PO
# (22_1_a2b_a)= Keep SENDA
# (22_1_a2b_b_2)= Keep PO * was worth of discussing, I avoided
#
# (22_1_a2a_4b_1)= Impute and keep the nearest
# (22_1_a2b_b_1)= Impute and keep the nearest
wo_imp_flow_fech_nac_fisc_vs_senda<-
flow_fech_nac_fisc_vs_senda %>%
dplyr::mutate(flowch_age=dplyr::case_when(is.na(fech_nac_fisc_vs_senda2$diff)~"22_1_a0a_Invalid_age_Kept_SENDA",c_22_1_a0ab==0 &c_22_1_a0cd==1~"22_1_a0c_<90_days_diff_Kept_SENDA",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==1~"22_1_a0ef_Original_TOP_C1_matched_age_Kept_PO",
c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==1&c_22_1_a_12_ab==1&grepl("both",c_22_1_a1a_1234)~"22_1_a1a_1_Kept_PO",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==1&c_22_1_a_12_ab==1& grepl("PO",c_22_1_a1a_1234)&nchar(c_22_1_a1a_1234_ab)<2~"22_1_a1a_2_Kept_PO",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==1&c_22_1_a_12_ab==1&grepl("SENDA",c_22_1_a1a_1234)~"22_1_a1a_3_Kept_SENDA",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==1&c_22_1_a_12_ab==1& nchar(c_22_1_a1a_1234)<2& nchar(c_22_1_a1a_1234_ab)>2~"22_1_a1a_4a_Kept_SENDA",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==1&c_22_1_a_12_ab==1& nchar(c_22_1_a1a_1234)<2& nchar(c_22_1_a1a_1234_ab)<2~"22_1_a1a_4b_Kept_PO",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==1&c_22_1_a_12_ab==0&nchar(c_22_1_a1a_1234_ab)>2~"22_1_a1b_a_Kept_SENDA",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==1&c_22_1_a_12_ab==0&nchar(c_22_1_a1a_1234_ab)<2~"22_1_a1b_b_Kept_PO",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==0&c_22_1_a_12_ab==1& grepl("both",c_22_1_a1a_1234)&nchar(c_22_1_a1a_1234_ab)>2~"22_1_a2a_1a_Kept_SENDA",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==0&c_22_1_a_12_ab==1& grepl("both",c_22_1_a1a_1234)&nchar(c_22_1_a1a_1234_ab)<2~"22_1_a2a_1b_Kept_PO",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==0&c_22_1_a_12_ab==1& grepl("PO",c_22_1_a1a_1234)&nchar(c_22_1_a1a_1234_ab)>2~"22_1_a2a_2a_Kept_SENDA",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==0&c_22_1_a_12_ab==1& grepl("PO",c_22_1_a1a_1234)&nchar(c_22_1_a1a_1234_ab)<2~"22_1_a2a_2b_Kept_PO",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==0&c_22_1_a_12_ab==1& grepl("SENDA",c_22_1_a1a_1234)~"22_1_a2a_3_Kept_SENDA",c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==0&c_22_1_a_12_ab==1& nchar(c_22_1_a1a_1234)<2& nchar(c_22_1_a1a_1234_ab)>2~"22_1_a2a_4a_Kept_SENDA",
c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==0&c_22_1_a_12_ab==1& nchar(c_22_1_a1a_1234)<2& nchar(c_22_1_a1a_1234_ab)<2&nchar(c_22_1_a2a_4b_12)>2~"22_1_a2a_4b_2_Kept_PO",
c_22_1_a0ab==0&c_22_1_a0cd==0&c_22_1_a0ef==0&c_22_1_a12==0&c_22_1_a_12_ab==0&nchar(c_22_1_a1a_1234_ab)>2~"22_1_a2b_a_Kept_SENDA",T~"Impute"
)) %>%
#saca
dplyr::filter(dplyr::case_when(flowch_age=="Impute"~F,T~T)) %>%
dplyr::mutate(imp_birth_date=dplyr::case_when(grepl("PO",flowch_age)~fec_nacimiento_simple,grepl("SENDA",flowch_age)~fech_nac)) %>%
dplyr::select(rut_enc_saf,imp_birth_date,fec_comision_simple,fech_ing,flowch_age,edad_al_ing,po_edad_al_ing,years_crime_po,years_crime_senda,edad_ini_cons)
For those 3,533 patients without sufficient information on the primacy of one birth date over another, we decided to impute these values based on a random forests model, with an out-of-bag score for model validation (ensuring that we get the better model with lower variance as well, thus, avoiding for overfitting). We chose the default amount of 100 trees for each forest and the default number maximum iterations of 10.
The database used for the imputation subset cases with the same birth dates and patients that needed an imputed age, but erasing the original and problematic birth date.
Show code
impute_age_po_senda_22_1_a2b_b<-
flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==0,nchar(c_22_1_a1a_1234_ab)<2) %>% dplyr::select(rut_enc_saf) %>% unlist() %>% as.character()
impute_age_po_senda_22_1_a2a_4b_1<-
flow_fech_nac_fisc_vs_senda %>% dplyr::filter(c_22_1_a0ab==0,c_22_1_a0cd==0,c_22_1_a0ef==0,c_22_1_a12==0,c_22_1_a_12_ab==1, nchar(c_22_1_a1a_1234)<2, nchar(c_22_1_a1a_1234_ab)<2, nchar(c_22_1_a2a_4b_12)<2) %>% dplyr::select(rut_enc_saf) %>% unlist() %>% as.character()
Base_fiscalia_v3_imp<-
Base_fiscalia_v3 %>%
dplyr::arrange(rut_enc_saf, fec_comision_simple) %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::mutate(n_dis_rucs=n_distinct(ruc)) %>%
dplyr::mutate(n_rows_hash=n(), rank_hash=row_number()) %>%
#count the times a HASH has been processed as an imputed
dplyr::mutate(n_imputed=sum(encontrado_como_imputado=="SI")) %>%
#get the second date of the comission of the crime
dplyr::mutate(lead_imputed=lead(fec_comision_simple)) %>%
dplyr::filter((n_imputed>0 & encontrado_como_imputado=="SI" & rank_hash== suppressWarnings( min(rank_hash[encontrado_como_imputado=="SI"])))| (n_imputed==0 & rank_hash== 1)) %>%
dplyr::ungroup() %>%
dplyr::left_join(subset(CONS_C1_df_dup_SEP_2020, subset= dup==1,select=c("hash_key", "fech_nac", "edad_ini_cons", "numero_de_hijos_mod", "escolaridad", "embarazo", "cat_ocupacional","estado_conyugal_2")),by=c("rut_enc_saf"="hash_key")) %>%
dplyr::mutate(fech_nac=as.Date(fech_nac)) %>%
#we kept the cases that had the same values and candidates for imputation
dplyr::filter(dplyr::case_when(fec_nacimiento_simple==fech_nac~T,rut_enc_saf %in% c(impute_age_po_senda_22_1_a2b_b,impute_age_po_senda_22_1_a2a_4b_1)~T, T~F)) %>%
dplyr::select(rut_enc_saf,edad_comision,cat_ocupacional,numero_de_hijos_mod, escolaridad, estado_conyugal_2, edad_ini_cons, clasificacion_penarpa_1_49) %>%
dplyr::mutate(edad_comision_imp= dplyr::case_when(rut_enc_saf %in% c(impute_age_po_senda_22_1_a2b_b,impute_age_po_senda_22_1_a2a_4b_1)~ NA_real_,T~edad_comision))
Base_fiscalia_v3_imp2<- Base_fiscalia_v3_imp %>% data.matrix()
paste0("The database with the HASHs that matched w/ C1 in the date of birth with data for imputation had ")
[1] "The database with the HASHs that matched w/ C1 in the date of birth with data for imputation had "Show code
[1] "66,285 rows. This database only have cases the first case as imputed of cases, "Show code
paste0("if they had any (if not, it will appear the first case as victim).")
[1] "if they had any (if not, it will appear the first case as victim)."Show code
paste0("Additionally, is matched with the first entry in the C1 dataset (the first).")
[1] "Additionally, is matched with the first entry in the C1 dataset (the first)."Show code
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#IMPUTE#_#_#_#_#_#_#_#_#_#_#_#_#_#_
if(isTRUE(getOption('knitr.in.progress'))==F){
job::job({
library(doParallel)
registerDoParallel(cores=7)
set.seed(2222)
system.time({
after_imp_Base_fiscalia_v3_imp <- missForest::missForest(Base_fiscalia_v3_imp2, #debe ser data matrix
maxiter= 10,#iteraciones máximas a hacer, por defecto es 10
ntree= 100,#número de árboles para cada bosque. Default 100
#xtrue= T,#opcional, un sirve para probar el desempeño, mostrará el error de imputación de cada iteración y el output contendrá el error de imputación.
parallelize="forest", # si se paraleliza de acuerdo a los cores
verbose=T
)
})
})
} else {
library(doParallel)
registerDoParallel(cores=6)
set.seed(2222)
system.time({
after_imp_Base_fiscalia_v3_imp <- missForest::missForest(Base_fiscalia_v3_imp2, #debe ser data matrix
maxiter= 10,#iteraciones máximas a hacer, por defecto es 10
ntree= 100,#número de árboles para cada bosque. Default 100
#xtrue= T,#opcional, un sirve para probar el desempeño, mostrará el error de imputación de cada iteración y el output contendrá el error de imputación.
parallelize="forest", # si se paraleliza de acuerdo a los cores
verbose=T
)
})
}
parallelizing computation of the random forest model objects
missForest iteration 1 in progress...done!
estimated error(s): 0.0001497471
difference(s): 5.284978e-09
time: 7014.42 seconds
missForest iteration 2 in progress...done!
estimated error(s): 0.0001353698
difference(s): 3.143776e-10
time: 6832.94 seconds
missForest iteration 3 in progress...done!
estimated error(s): 0.0001281562
difference(s): 4.276481e-10
time: 7457.05 seconds user system elapsed
17.04 8.25 21304.44 Show code
message(paste0("(NRMSE= ",round(as.numeric(after_imp_Base_fiscalia_v3_imp$OOBerror),5),")"))
#https://www.rdocumentation.org/packages/missForest/versions/1.4/topics/missForest
#https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf
#https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201904
#https://medium.datadriveninvestor.com/missforest-one-of-the-best-imputational-methods-4a01170899c8
#https://academic.oup.com/bioinformatics/article/28/1/112/219101?login=true
AS a measure of the imputation error, we got the NRMSE, or the root mean squared difference between the new imputed ages and the previous ones divided by the variance of the true values. Then, we compared the absolute difference between the imputed age and the age a crime was committed (or was a victim of one) calculated from the birth date of PO or SENDA data, and chose the birth date with the closest value to the imputed, and calculated the birth date.
Show code
after_imp_Base_fiscalia_v3_db<-
cbind.data.frame(rut_enc_saf=Base_fiscalia_v3_imp$rut_enc_saf,as.data.frame(after_imp_Base_fiscalia_v3_imp$ximp)[,-1]) %>%
dplyr::filter(dplyr::case_when(rut_enc_saf %in% c(impute_age_po_senda_22_1_a2b_b,impute_age_po_senda_22_1_a2a_4b_1)~T,T~F)) %>%
#edad_comision_imp: imputed age of comission of the crime
dplyr::select(rut_enc_saf,edad_comision_imp) %>%
#dplyr::rename("edad_comision_imp"="edad_comision") %>%
dplyr::left_join(flow_fech_nac_fisc_vs_senda, by="rut_enc_saf") %>%
#If the absolute difference between the imputed age and the age a crime was committed (or was a victim of one) from the birth date of PO data is less than the absolute difference between the imputed age and the age a crime was committed (or was a victim of one) from the birth date of SENDA data
dplyr::mutate(closest_to_imp_bdate=dplyr::case_when(abs(edad_comision_imp-years_crime_po)<abs(edad_comision_imp-years_crime_senda)~fec_nacimiento_simple,T~fech_nac)) %>%
dplyr::mutate(flowch_age= "22_1_Impute_birth_date_forest") %>%
dplyr::mutate(fech_nac_imp= fec_comision_simple- (edad_comision_imp*365.25)) %>%
dplyr::select(rut_enc_saf,fech_nac_imp,closest_to_imp_bdate,fec_nacimiento_simple,fech_nac, fec_comision_simple,fech_ing,flowch_age,edad_al_ing,po_edad_al_ing,years_crime_po,years_crime_senda,edad_ini_cons,edad_comision_imp,c_22_1_a1a_1234_ab) %>%
dplyr::mutate(imp_po_edad_al_ing=as.numeric(fech_ing-fech_nac_imp)/365.25) %>%
dplyr::mutate(c_22_1_a1a_1234_ab2=dplyr::case_when(imp_po_edad_al_ing<edad_ini_cons~"admitted before started using", T~"")) %>%
purrr::when(dplyr::filter(.,nchar(c_22_1_a1a_1234_ab2)>2) %>% nrow()>0 ~ stop("imputed age at admission lower than the age of onset of drug use in imputed"), ~.) %>%
dplyr::select(rut_enc_saf, flowch_age,fech_nac_imp,fech_ing,edad_ini_cons) %>% #7,972 wo_imp
dplyr::rename("imp_birth_date"="fech_nac_imp") %>%
dplyr::bind_rows(wo_imp_flow_fech_nac_fisc_vs_senda[,c("rut_enc_saf","flowch_age","imp_birth_date","fech_ing","edad_ini_cons")]) %>%
dplyr::mutate(imp_edad_al_ing=as.numeric(fech_ing-imp_birth_date)/365.25) %>%
dplyr::mutate(c_22_1_a1a_1234_ab3=dplyr::case_when(imp_edad_al_ing<edad_ini_cons~"admitted before started using", T~"")) %>%
purrr::when(dplyr::filter(.,nchar(c_22_1_a1a_1234_ab3)>2) %>% nrow()>0 ~ stop("imputed age at admission lower than the age of onset of drug use after imputation"), ~.)
Modify the database according to the imputation
Show code
#https://exchangetuts.com/how-to-add-code-folding-to-output-chunks-in-rmarkdown-html-documents-1639691105162168
tab_agr_ter<-
Base_fiscalia_v3 %>%
dplyr::left_join(after_imp_Base_fiscalia_v3_db[,c("rut_enc_saf","imp_birth_date","flowch_age")], by="rut_enc_saf") %>%
dplyr::rename("obs"="flowch_age") %>%
dplyr::mutate(imp_birth_date=dplyr::case_when(!is.na(imp_birth_date)~imp_birth_date,T~fec_nacimiento_simple))%>% dplyr::mutate(edad_comision_imp=as.numeric(fec_comision_simple-imp_birth_date)/365.25) %>% dplyr::mutate(edad_ter_rel_imp=as.numeric(termino_relacion_simple-imp_birth_date)/365.25) %>%
#arrange the rut from the first date of comission of a crime, but we are not detecting if he/she is the victim or not
dplyr::arrange(rut_enc_saf, edad_comision_imp) %>% #566884
dplyr::filter(dplyr::case_when(is.na(edad_comision_imp)~T,T~F)) %>% janitor::tabyl(agrupa_terminos) %>%
dplyr::left_join(Base_fiscalia_v3 %>% janitor::tabyl(agrupa_terminos), by="agrupa_terminos") %>%
dplyr::rename("Partial Perc"="percent.x","Total Perc"="percent.y") %>%
dplyr::select(agrupa_terminos, `Partial Perc`, `Total Perc`) %>%
dplyr::mutate(across(where(is.numeric),scales::percent,0.1))
tab_agr_ter %>% knitr::kable("html") %>% kableExtra::kable_classic()
| agrupa_terminos | Partial Perc | Total Perc |
|---|---|---|
| ACUERDO REPARATORIO | 2.9% | 2.4% |
| AGRUPACI¿N A OTRO CASO | 5.0% | 5.0% |
| ANULACI¿N ADMINISTRATIVA | 42.5% | 0.3% |
| ARCHIVO PROVISIONAL | 2.9% | 26.3% |
| DECISI¿N DE NO PERSEVERAR | 2.5% | 5.9% |
| FACULTAD PARA NO INVESTIGAR | 0.4% | 7.4% |
| OTRAS CAUSALES DE T¿RMINO | 0.8% | 0.2% |
| PRINCIPIO DE OPORTUNIDAD | 3.3% | 8.0% |
| SENTENCIA DEFINITIVA ABSOLUTORIA | 3.3% | 2.1% |
| SENTENCIA DEFINITIVA CONDENATORIA | 17.1% | 27.7% |
| SOBRESEIMIENTO DEFINITIVO | 2.9% | 5.0% |
| SOBRESEIMIENTO DEFINITIVO 240 | 15.8% | 7.9% |
| SUSPENSI¿N CONDICIONAL DEL PROCEDIMIENTO | 0.4% | 1.3% |
We decided to eliminate those cases with missing birth dates that matched with the original C1 and TOP databases only (13 patients). Additionally, we eliminated those cases with no date of comission of the crime (240 entries). Must consider that much more had annulment of the administrative procedures (42.5% vs. 0.3%) or dismissal of proceedings art.240 (15.8% vs. 7.9%).
Show code
invisible("erase entries with missing values in fec_comision_simple y termino_relacion_simple")
Base_fiscalia_v3 %>%
dplyr::left_join(after_imp_Base_fiscalia_v3_db[,c("rut_enc_saf","imp_birth_date","flowch_age")], by="rut_enc_saf") %>%
dplyr::rename("obs"="flowch_age") %>%
dplyr::mutate(imp_birth_date=dplyr::case_when(!is.na(imp_birth_date)~imp_birth_date,T~fec_nacimiento_simple))%>% dplyr::mutate(edad_comision_imp=as.numeric(fec_comision_simple-imp_birth_date)/365.25) %>% dplyr::mutate(edad_ter_rel_imp=as.numeric(termino_relacion_simple-imp_birth_date)/365.25) %>%
#arrange the rut from the first date of comission of a crime, but we are not detecting if he/she is the victim or not
dplyr::arrange(rut_enc_saf, edad_comision_imp) %>% #566884
dplyr::filter(dplyr::case_when(!is.na(edad_comision_imp)~T,T~F)) %>% #566644
dplyr::filter(imp_birth_date!="1900-01-01") %>%
assign("Base_fiscalia_v4",.,envir = .GlobalEnv)
if(
Base_fiscalia_v4 %>%
dplyr::filter(imp_birth_date=="1900-01-01") %>%
dplyr::left_join(CONS_C1_df_dup_SEP_2020[,c("hash_key","fech_nac")], by=c("rut_enc_saf"="hash_key")) %>% dplyr::distinct(rut_enc_saf) %>% nrow()>0){stop("Error: cases with missing values")}
Show code
rbind(
cbind(cat="Date of comission of the crime",
Base_fiscalia_v4 %>%
dplyr::summarise(min = min(fec_comision_simple, na.rm=T),
p025=as.Date(quantile(unclass(fec_comision_simple), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(fec_comision_simple), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(fec_comision_simple), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(fec_comision_simple), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(fec_comision_simple), .975, na.rm=T), origin = "1970-01-01"),
max = max(fec_comision_simple, na.rm=T))),
cbind(cat="Date of termination of the relationship",
Base_fiscalia_v4 %>%
dplyr::summarise(min = min(termino_relacion_simple, na.rm=T),
p025=as.Date(quantile(unclass(termino_relacion_simple), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(termino_relacion_simple), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(termino_relacion_simple), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(termino_relacion_simple), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(termino_relacion_simple), .975, na.rm=T), origin = "1970-01-01"),
max = max(termino_relacion_simple, na.rm=T))),
cbind(cat="Date of cbiorelacion",
Base_fiscalia_v4 %>%
dplyr::summarise(min = min(fec_cbiorelacion_simple, na.rm=T),
p025=as.Date(quantile(unclass(fec_cbiorelacion_simple), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(fec_cbiorelacion_simple), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(fec_cbiorelacion_simple), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(fec_cbiorelacion_simple), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(fec_cbiorelacion_simple), .975, na.rm=T), origin = "1970-01-01"),
max = max(fec_cbiorelacion_simple, na.rm=T))),
cbind(cat="Date of birth",
Base_fiscalia_v4 %>%
dplyr::summarise(min = min(imp_birth_date, na.rm=T),
p025=as.Date(quantile(unclass(imp_birth_date), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(imp_birth_date), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(imp_birth_date), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(imp_birth_date), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(imp_birth_date), .975, na.rm=T), origin = "1970-01-01"),
max = max(imp_birth_date, na.rm=T)))) %>%
knitr::kable(format="html",caption= "Summary of Dates") %>% #,col.names=c("Variables","Residential", "Ambulatory", "p-value")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover","condensed"),font_size= 12) %>%
kableExtra::group_rows("After imputation database",1,3) %>%
kableExtra::footnote(general = paste0("Total entries: ",format(nrow(Base_fiscalia_v4), big.mark=","),"; Total patients: ",format(length(unique(Base_fiscalia_v4$rut_enc_saf)), big.mark=",")))
| cat | min | p025 | p25 | p50 | p75 | p975 | max |
|---|---|---|---|---|---|---|---|
| After imputation database | |||||||
| Date of comission of the crime | 1900-01-24 | 2009-04-06 | 2011-12-08 | 2014-04-18 | 2016-11-07 | 2019-05-28 | 2019-11-13 |
| Date of termination of the relationship | 2002-10-24 | 2010-04-09 | 2012-06-04 | 2014-11-10 | 2017-05-30 | 2019-09-13 | 2021-07-20 |
| Date of cbiorelacion | 2002-10-24 | 2010-04-09 | 2012-06-04 | 2014-11-10 | 2017-05-30 | 2019-09-13 | 2021-07-20 |
| Date of birth | 1930-01-18 | 1959-09-25 | 1975-08-16 | 1982-12-21 | 1988-11-04 | 1996-06-23 | 2005-02-25 |
| Note: | |||||||
| Total entries: 566,583; Total patients: 74,771 | |||||||
- Entries that committed a crime at less than 14 years old 982.
- Entries that committed a crime at less than 14 years old (of cases w/ 2 different birth dates) 142.
Show code
errores_edad<-
Base_fiscalia_v4 %>% dplyr::filter(edad_comision_imp<14) %>% distinct(rut_enc_saf)
# wo_imp_flow_fech_nac_fisc_vs_senda %>%
# dplyr::filter(rut_enc_saf %in% as.character(unlist(errores_edad))) %>% View()
cbind(n=c("Birth date","Age of commission of crime","Admission date"),
rbind(
wo_imp_flow_fech_nac_fisc_vs_senda %>%
dplyr::filter(rut_enc_saf %in% as.character(unlist(errores_edad))) %>%
dplyr::summarise(min = min(imp_birth_date, na.rm=T),
p025=as.Date(quantile(unclass(imp_birth_date), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(imp_birth_date), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(imp_birth_date), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(imp_birth_date), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(imp_birth_date), .975, na.rm=T), origin = "1970-01-01"),
max = max(imp_birth_date, na.rm=T)),
wo_imp_flow_fech_nac_fisc_vs_senda %>%
dplyr::filter(rut_enc_saf %in% as.character(unlist(errores_edad))) %>%
dplyr::summarise(min = min(fec_comision_simple, na.rm=T),
p025=as.Date(quantile(unclass(fec_comision_simple),.025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(fec_comision_simple), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(fec_comision_simple), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(fec_comision_simple), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(fec_comision_simple),.975, na.rm=T), origin = "1970-01-01"),
max = max(fec_comision_simple, na.rm=T)),
wo_imp_flow_fech_nac_fisc_vs_senda %>%
dplyr::filter(rut_enc_saf %in% as.character(unlist(errores_edad))) %>%
dplyr::summarise(min = min(fech_ing, na.rm=T),
p025=as.Date(quantile(unclass(fech_ing),.025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(fech_ing), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(fech_ing), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(fech_ing), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(fech_ing),.975, na.rm=T), origin = "1970-01-01"),
max = max(fech_ing, na.rm=T))
))%>%
knitr::kable(format="html",caption= paste0("Summary of Dates w/ aberrant age of comission of crime (n=", nrow(dplyr::filter(Base_fiscalia_v4,edad_comision_imp<14)),")")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover","condensed"),font_size= 12)
| n | min | p025 | p25 | p50 | p75 | p975 | max |
|---|---|---|---|---|---|---|---|
| Birth date | 1981-09-24 | 1984-02-21 | 1993-10-08 | 1997-04-05 | 1998-11-19 | 2001-06-12 | 2001-08-11 |
| Age of commission of crime | 1994-06-06 | 2001-01-01 | 2010-06-07 | 2011-12-26 | 2014-05-06 | 2017-12-29 | 2019-05-20 |
| Admission date | 2009-04-03 | 2012-02-29 | 2014-11-07 | 2017-04-03 | 2018-06-24 | 2019-10-08 | 2019-10-16 |
Show code
Base_fiscalia_v5<-
Base_fiscalia_v4 %>% dplyr::filter(edad_comision_imp>=14) %>% dplyr::ungroup() %>%
dplyr::mutate(familia_delito_rec=dplyr::case_when(grepl("LESA HUMANIDAD",familia_delito)~"VIOLENT CRIME",
grepl("SEXUALES",familia_delito)~"VIOLENT CRIME",
grepl("ROBOS$",familia_delito)~"VIOLENT CRIME",
grepl("LESIONES$",familia_delito)~"VIOLENT CRIME",
grepl("HOMICIDIOS$",familia_delito)~"VIOLENT CRIME",
grepl("DROGAS$",familia_delito)~"DRUG-RELATED CRIME",
grepl("RELEVANCIA",familia_delito)& grepl("DROGAS",gls_materia)~"DRUG-RELATED CRIME",
grepl("RELEVANCIA",familia_delito)& grepl("PRESUNTA",gls_materia)~"VIOLENT CRIME",
grepl("RELEVANCIA",familia_delito)& grepl("MUERTES",gls_materia)~"VIOLENT CRIME",
T~"OTHER CHARGES")) %>%
dplyr::left_join(dplyr::rename(CONS_C1_df_dup_FEB_2020_prev25b[,c("hash_key","obs")],"obs_FEB"="obs") %>% dplyr::group_by(hash_key) %>% dplyr::slice(1) %>% dplyr::ungroup(),by=c("rut_enc_saf"="hash_key")) %>%
dplyr::mutate(obs=dplyr::case_when(obs==obs_FEB~obs_FEB,
is.na(obs)~obs_FEB,
!is.na(obs)& grepl("^;",obs_FEB)~paste0(obs,obs_FEB),
!is.na(obs)& !grepl("^;",obs_FEB)~paste0(obs,";",obs_FEB),
T~obs)) %>%
dplyr::select(-obs_FEB)
Show code
invisible("Se eliminan las edades de las bases de datos que no calzan (<14 a), los reemplazos que hice en general no dejan mucha opción para reemplazar la fecha de nacimiento por otra mejor.")
invisible(paste0("Hasta ahora he perdido ", nrow(Base_fiscalia_v2)-nrow(Base_fiscalia_v5)," observaciones, de ",length(unique(Base_fiscalia_v2$rut_enc_saf))-length(unique(Base_fiscalia_v5$rut_enc_saf))," usuarios."))#2695 observaciones que se perdieron, y #88 usuarios que se perdieron
# Original Prosecutors Office
# (n = 560,959;
# Causes= 488,613;
# Rel.=560,952;
# RUC_Vic_Imp=534,710;
# individuals= 74,653)
message(paste0('Original Prosecutors Office after modification of dates (end judiciary relationship <2019-11-13 )\n(n = ',format(nrow(subset(Base_fiscalia_v5, termino_relacion_simple<"2019-11-13")),big.mark=","),
';\nCauses= ',subset(Base_fiscalia_v5, termino_relacion_simple<"2019-11-13")%>% dplyr::distinct(ruc)%>% nrow() %>% format(big.mark=','),
';\nRel.=',subset(Base_fiscalia_v5, termino_relacion_simple<"2019-11-13")%>%dplyr::distinct(idrelacion)%>%nrow()%>%format(big.mark=','),
';\nRUC_Vic_Imp=',subset(Base_fiscalia_v5, termino_relacion_simple<"2019-11-13")%>%dplyr::mutate(rel=paste0(ruc,"_",idsujeto_victima,"_",idsujeto_imputado,"_","iddelito"))%>%dplyr::distinct(rel)%>%nrow()%>%format(big.mark=','),
';\nindividuals= ',subset(Base_fiscalia_v5, termino_relacion_simple<"2019-11-13")%>% dplyr::distinct(rut_enc_saf)%>% nrow() %>% format(big.mark=','),')'))
message(paste0('Original Prosecutors Office after modification of dates (crimes committed <2019-11-13)\n(n = ',format(nrow(subset(Base_fiscalia_v5, fec_comision_simple<"2019-11-13")),big.mark=","),
';\nCauses= ',subset(Base_fiscalia_v5, fec_comision_simple<"2019-11-13")%>% dplyr::distinct(ruc)%>% nrow() %>% format(big.mark=','),
';\nRel.=',subset(Base_fiscalia_v5, fec_comision_simple<"2019-11-13")%>%dplyr::distinct(idrelacion)%>%nrow()%>%format(big.mark=','),
';\nRUC_Vic_Imp=',subset(Base_fiscalia_v5, fec_comision_simple<"2019-11-13")%>%dplyr::mutate(rel=paste0(ruc,"_",idsujeto_victima,"_",idsujeto_imputado,"_","iddelito"))%>%dplyr::distinct(rel)%>%nrow()%>%format(big.mark=','),
';\nindividuals= ',subset(Base_fiscalia_v5, fec_comision_simple<"2019-11-13")%>% dplyr::distinct(rut_enc_saf)%>% nrow() %>% format(big.mark=','),')'))
Show code
rbind(
cbind(cat="Date of comission of the crime",
Base_fiscalia_v5 %>%
dplyr::summarise(min = min(fec_comision_simple, na.rm=T),
p025=as.Date(quantile(unclass(fec_comision_simple), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(fec_comision_simple), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(fec_comision_simple), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(fec_comision_simple), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(fec_comision_simple), .975, na.rm=T), origin = "1970-01-01"),
max = max(fec_comision_simple, na.rm=T))),
cbind(cat="Date of termination of the relationship",
Base_fiscalia_v5 %>%
dplyr::summarise(min = min(termino_relacion_simple, na.rm=T),
p025=as.Date(quantile(unclass(termino_relacion_simple), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(termino_relacion_simple), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(termino_relacion_simple), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(termino_relacion_simple), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(termino_relacion_simple), .975, na.rm=T), origin = "1970-01-01"),
max = max(termino_relacion_simple, na.rm=T))),
cbind(cat="Date of cbiorelacion",
Base_fiscalia_v5 %>%
dplyr::summarise(min = min(fec_cbiorelacion_simple, na.rm=T),
p025=as.Date(quantile(unclass(fec_cbiorelacion_simple), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(fec_cbiorelacion_simple), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(fec_cbiorelacion_simple), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(fec_cbiorelacion_simple), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(fec_cbiorelacion_simple), .975, na.rm=T), origin = "1970-01-01"),
max = max(fec_cbiorelacion_simple, na.rm=T))),
cbind(cat="Date of birth",
Base_fiscalia_v5 %>%
dplyr::summarise(min = min(imp_birth_date, na.rm=T),
p025=as.Date(quantile(unclass(imp_birth_date), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(imp_birth_date), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(imp_birth_date), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(imp_birth_date), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(imp_birth_date), .975, na.rm=T), origin = "1970-01-01"),
max = max(imp_birth_date, na.rm=T)))) %>%
knitr::kable(format="html",caption= "Summary of Dates (after correcting dates)") %>% #,col.names=c("Variables","Residential", "Ambulatory", "p-value")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover","condensed"),font_size= 12) %>%
kableExtra::group_rows("After imputation database",1,3) %>%
kableExtra::footnote(general = paste0("Total entries: ",format(nrow(Base_fiscalia_v5), big.mark=","),"; Total patients: ",format(length(unique(Base_fiscalia_v5$rut_enc_saf)), big.mark=",")))
| cat | min | p025 | p25 | p50 | p75 | p975 | max |
|---|---|---|---|---|---|---|---|
| After imputation database | |||||||
| Date of comission of the crime | 1972-12-21 | 2009-04-14 | 2011-12-10 | 2014-04-21 | 2016-11-09 | 2019-05-28 | 2019-11-13 |
| Date of termination of the relationship | 2002-10-24 | 2010-04-09 | 2012-06-06 | 2014-11-11 | 2017-05-31 | 2019-09-13 | 2021-07-20 |
| Date of cbiorelacion | 2002-10-24 | 2010-04-09 | 2012-06-06 | 2014-11-12 | 2017-05-31 | 2019-09-13 | 2021-07-20 |
| Date of birth | 1930-01-18 | 1959-09-20 | 1975-08-08 | 1982-12-15 | 1988-10-26 | 1996-05-19 | 2001-09-29 |
| Note: | |||||||
| Total entries: 565,601; Total patients: 74,745 | |||||||
Explore relationships
Explore Results of the Preliminary Matching
Date of birth, found as an imputed/victim
Show code
#https://coolors.co/0a1128-001f54-034078-1282a2-c1dbe3
#c("pais", "encontrado_como_victima", "gls_tipo_sujeto_vic", "gls_tipo_imputado", "gls_region", "familia_delito", "agrupa_terminos", "sexo", "edad_comision", "region_delito")
i<- "encontrado_como_imputado"
min_plot<-1940
max_plot<-2002
casos_no_cubiertos<-
Base_fiscalia_v5 %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(imp_birth_date)) %>%
dplyr::mutate(grupo_var=get(i))%>%
dplyr::group_by(fec_nacimiento_qrt, grupo_var)%>%
dplyr::summarise(n_2_grupos=n())%>%
dplyr::ungroup()%>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::mutate(freq = (n_2_grupos / sum(n_2_grupos)))%>%
dplyr::filter(fec_nacimiento_qrt<min_plot|fec_nacimiento_qrt>=max_plot) %>%
dplyr::ungroup()%>%
dplyr::summarise(sum_total=sum(n_2_grupos))%>%
unlist()
#colorspace::diverge_hcl(length(table(Base_fiscalia_v4[[i]])),h=c(0,-100),l=c(75,30),c=c(40,80),power=1)
Base_fiscalia_v5 %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(imp_birth_date)) %>%
dplyr::mutate(grupo_var=get(i))%>%
dplyr::group_by(fec_nacimiento_qrt, grupo_var)%>%
dplyr::summarise(n_2_grupos=n())%>%
dplyr::ungroup()%>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::mutate(freq = (n_2_grupos / sum(n_2_grupos)))%>%
dplyr::filter(fec_nacimiento_qrt>=min_plot) %>%
ggplot2::ggplot(aes(x = fec_nacimiento_qrt, y = freq,fill=grupo_var))+
geom_area(alpha=0.6 , size=.5, colour="white") +
scale_fill_manual(values= colorspace::rainbow_hcl(length(table(Base_fiscalia_v5[[i]])))) +
sjPlot::theme_sjplot2() +
scale_x_yearqtr(format="%YQ%q", n=18,
limits=c(zoo::as.yearqtr(paste0(min_plot,"-01-01")),
max=zoo::as.yearqtr(paste0(max_plot,"-01-01"))))+
scale_y_continuous(limits=c(0,1),labels = scales::percent)+
labs(x="",y="Percentages",
caption= paste0("Note. Cases born between ",min_plot," to ",max_plot,", but ignoring the rest: ", casos_no_cubiertos,
";\n",
"Percentages by year and quarter"))+
#ylim(0,101)+
# scale_y_continuous(limits=c(0,1),labels = scales::percent) +
theme(legend.position="bottom")+
guides(fill=guide_legend(ncol=3))+
theme(legend.title = NULL,
legend.text = element_text(size = 55),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank())+
theme(axis.text.x = element_text(vjust = 0.5,hjust = 0.5,angle = 60)) +
theme(text=element_text(size=74))+
theme(plot.caption = element_text(hjust = 0, lineheight = .3))
Figure 4: Date of birth and suspicious of commiting a crime
Show code
NULLDate of birth, Aggregated judgment at the end of judicial proceedings
Show code
#https://coolors.co/0a1128-001f54-034078-1282a2-c1dbe3
#c("pais", "encontrado_como_victima", "gls_tipo_sujeto_vic", "gls_tipo_imputado", "gls_region", "familia_delito", "agrupa_terminos", "sexo", "edad_comision", "region_delito")
i<- "agrupa_terminos"
min_plot<-1950
max_plot<-2002
casos_no_cubiertos<-
Base_fiscalia_v5 %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(imp_birth_date)) %>%
dplyr::mutate(grupo_var=get(i))%>%
dplyr::group_by(fec_nacimiento_qrt, grupo_var)%>%
dplyr::summarise(n_2_grupos=n())%>%
dplyr::ungroup()%>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::mutate(freq = (n_2_grupos / sum(n_2_grupos)))%>%
dplyr::filter(fec_nacimiento_qrt<min_plot|fec_nacimiento_qrt>=max_plot) %>%
dplyr::ungroup()%>%
dplyr::summarise(sum_total=sum(n_2_grupos))%>%
unlist()
#colorspace::diverge_hcl(length(table(Base_fiscalia_v5[[i]])),h=c(0,-100),l=c(75,30),c=c(40,80),power=1)
Base_fiscalia_v5 %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(imp_birth_date)) %>%
dplyr::mutate(grupo_var=get(i))%>%
dplyr::group_by(fec_nacimiento_qrt, grupo_var)%>%
dplyr::summarise(n_2_grupos=n())%>%
dplyr::ungroup()%>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::mutate(freq = (n_2_grupos / sum(n_2_grupos)))%>%
dplyr::filter(fec_nacimiento_qrt>=min_plot) %>%
ggplot2::ggplot(aes(x = fec_nacimiento_qrt, y = freq,fill=grupo_var))+
geom_area(alpha=0.6 , size=.5, colour="white") +
scale_fill_manual(values= colorspace::rainbow_hcl(length(table(Base_fiscalia_v5[[i]])))) +
sjPlot::theme_sjplot2() +
scale_x_yearqtr(format="%YQ%q", n=18,
limits=c(zoo::as.yearqtr(paste0(min_plot,"-01-01")),
max=zoo::as.yearqtr(paste0(max_plot,"-01-01"))))+
scale_y_continuous(limits=c(0,1),labels = scales::percent)+
labs(x="",y="Percentages",
caption= paste0("Note. Cases born between ",min_plot," to ",max_plot,", but ignoring the rest: ", casos_no_cubiertos,
";\n",
"Percentages by year and quarter"))+
#ylim(0,101)+
# scale_y_continuous(limits=c(0,1),labels = scales::percent) +
theme(legend.position="bottom")+
guides(fill=guide_legend(ncol=3))+
theme(legend.title = NULL,
legend.text = element_text(size = 45),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank())+
theme(axis.text.x = element_text(vjust = 0.5,hjust = 0.5,angle = 60)) +
theme(text=element_text(size=74)) +
theme(plot.caption = element_text(hjust = 0, lineheight = .3))
Figure 5: Date of birth and end of judicial proceedings
Show code
NULLDate of birth, Sex and found as an imputed
Show code
#https://coolors.co/0a1128-001f54-034078-1282a2-c1dbe3
#c("pais", "encontrado_como_victima", "gls_tipo_sujeto_vic", "gls_tipo_imputado", "gls_region", "familia_delito", "agrupa_terminos", "sexo", "edad_comision", "region_delito")
i<- "sexo"
min_plot<-1945
max_plot<-2002
casos_no_cubiertos<-
Base_fiscalia_v5 %>%
dplyr::filter(encontrado_como_imputado=="SI") %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(imp_birth_date)) %>%
dplyr::mutate(grupo_var=get(i))%>%
dplyr::group_by(fec_nacimiento_qrt, grupo_var)%>%
dplyr::summarise(n_2_grupos=n())%>%
dplyr::ungroup()%>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::mutate(freq = (n_2_grupos / sum(n_2_grupos)))%>%
dplyr::filter(fec_nacimiento_qrt<min_plot|fec_nacimiento_qrt>=max_plot) %>%
dplyr::ungroup()%>%
dplyr::summarise(sum_total=sum(n_2_grupos))%>%
unlist()
#colorspace::diverge_hcl(length(table(Base_fiscalia_v5[[i]])),h=c(0,-100),l=c(75,30),c=c(40,80),power=1)
Base_fiscalia_v5 %>%
dplyr::filter(encontrado_como_imputado=="SI") %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(imp_birth_date)) %>%
dplyr::mutate(grupo_var=get(i))%>%
dplyr::group_by(fec_nacimiento_qrt, grupo_var)%>%
dplyr::summarise(n_2_grupos=n())%>%
dplyr::ungroup()%>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::mutate(freq = (n_2_grupos / sum(n_2_grupos)))%>%
dplyr::filter(fec_nacimiento_qrt>=min_plot) %>%
ggplot2::ggplot(aes(x = fec_nacimiento_qrt, y = freq,fill=grupo_var))+
geom_area(alpha=0.6 , size=.5, colour="white") +
scale_fill_manual(values= colorspace::rainbow_hcl(length(table(Base_fiscalia_v5[[i]])))) +
sjPlot::theme_sjplot2() +
scale_x_yearqtr(format="%YQ%q", n=18,
limits=c(zoo::as.yearqtr(paste0(min_plot,"-01-01")),
max=zoo::as.yearqtr(paste0(max_plot,"-01-01"))))+
scale_y_continuous(limits=c(0,1),labels = scales::percent)+
labs(x="",y="Percentages",
caption= paste0("Note. Cases born between ",min_plot," to ",max_plot,", but ignoring the rest: ", casos_no_cubiertos, " entries",
";\n",
"Percentages by year and quarter"))+
#ylim(0,101)+
# scale_y_continuous(limits=c(0,1),labels = scales::percent) +
theme(legend.position="bottom")+
guides(fill=guide_legend(ncol=3))+
theme(legend.title = NULL,
legend.text = element_text(size = 55),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank())+
theme(axis.text.x = element_text(vjust = 0.5,hjust = 0.5,angle = 60)) +
theme(text=element_text(size=74))+
theme(plot.caption = element_text(hjust = 0, lineheight = .3))
Figure 6: Sex, date of birth in suspicious of commiting a crime
Show code
NULLDate of birth, Sex and found as a victim
Show code
#https://coolors.co/0a1128-001f54-034078-1282a2-c1dbe3
#c("pais", "encontrado_como_victima", "gls_tipo_sujeto_vic", "gls_tipo_imputado", "gls_region", "familia_delito", "agrupa_terminos", "sexo", "edad_comision", "region_delito")
i<- "sexo"
min_plot<-1945
max_plot<-2002
casos_no_cubiertos<-
Base_fiscalia_v5 %>%
dplyr::filter(encontrado_como_imputado=="NO") %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(imp_birth_date)) %>%
dplyr::mutate(grupo_var=get(i))%>%
dplyr::group_by(fec_nacimiento_qrt, grupo_var)%>%
dplyr::summarise(n_2_grupos=n())%>%
dplyr::ungroup()%>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::mutate(freq = (n_2_grupos / sum(n_2_grupos)))%>%
dplyr::filter(fec_nacimiento_qrt<min_plot|fec_nacimiento_qrt>=max_plot) %>%
dplyr::ungroup()%>%
dplyr::summarise(sum_total=sum(n_2_grupos))%>%
unlist()
#colorspace::diverge_hcl(length(table(Base_fiscalia_v5[[i]])),h=c(0,-100),l=c(75,30),c=c(40,80),power=1)
Base_fiscalia_v5 %>%
dplyr::filter(encontrado_como_imputado=="NO") %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(imp_birth_date)) %>%
dplyr::mutate(grupo_var=get(i))%>%
dplyr::group_by(fec_nacimiento_qrt, grupo_var)%>%
dplyr::summarise(n_2_grupos=n())%>%
dplyr::ungroup()%>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::mutate(freq = (n_2_grupos / sum(n_2_grupos)))%>%
dplyr::filter(fec_nacimiento_qrt>=min_plot) %>%
ggplot2::ggplot(aes(x = fec_nacimiento_qrt, y = freq,fill=grupo_var))+
geom_area(alpha=0.6 , size=.5, colour="white") +
scale_fill_manual(values= colorspace::rainbow_hcl(length(table(Base_fiscalia_v5[[i]])))) +
sjPlot::theme_sjplot2() +
scale_x_yearqtr(format="%YQ%q", n=18,
limits=c(zoo::as.yearqtr(paste0(min_plot,"-01-01")),
max=zoo::as.yearqtr(paste0(max_plot,"-01-01"))))+
scale_y_continuous(limits=c(0,1),labels = scales::percent)+
labs(x="",y="Percentages",
caption= paste0("Note. Cases born between ",min_plot," to ",max_plot,", but ignoring the rest: ", casos_no_cubiertos, " entries",
";\n",
"Percentages by year and quarter"))+
#ylim(0,101)+
# scale_y_continuous(limits=c(0,1),labels = scales::percent) +
theme(legend.position="bottom")+
guides(fill=guide_legend(ncol=3))+
theme(legend.title = NULL,
legend.text = element_text(size = 55),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank())+
theme(axis.text.x = element_text(vjust = 0.5,hjust = 0.5,angle = 60)) +
theme(text=element_text(size=74))+
theme(plot.caption = element_text(hjust = 0, lineheight = .3))
Figure 7: Sex, date of birth in involved as a victim
Show code
NULLDate of birth, Sex and type of crime
Show code
#https://coolors.co/0a1128-001f54-034078-1282a2-c1dbe3
#c("pais", "encontrado_como_victima", "gls_tipo_sujeto_vic", "gls_tipo_imputado", "gls_region", "familia_delito", "agrupa_terminos", "sexo", "edad_comision", "region_delito")
i<- "familia_delito_rec"
min_plot<-1957
max_plot<-2002
casos_no_cubiertos<-
Base_fiscalia_v5 %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(imp_birth_date)) %>%
dplyr::mutate(grupo_var=get(i))%>%
dplyr::group_by(fec_nacimiento_qrt, grupo_var)%>%
dplyr::summarise(n_2_grupos=n())%>%
dplyr::ungroup()%>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::mutate(freq = (n_2_grupos / sum(n_2_grupos)))%>%
dplyr::filter(fec_nacimiento_qrt<min_plot|fec_nacimiento_qrt>=max_plot) %>%
dplyr::ungroup()%>%
dplyr::summarise(sum_total=sum(n_2_grupos))%>%
unlist()
#colorspace::diverge_hcl(length(table(Base_fiscalia_v5[[i]])),h=c(0,-100),l=c(75,30),c=c(40,80),power=1)
Base_fiscalia_v5 %>%
dplyr::mutate(fec_nacimiento_qrt=zoo::as.yearqtr(imp_birth_date)) %>%
dplyr::mutate(grupo_var=get(i))%>%
dplyr::group_by(fec_nacimiento_qrt, grupo_var)%>%
dplyr::summarise(n_2_grupos=n())%>%
dplyr::ungroup()%>%
dplyr::group_by(fec_nacimiento_qrt)%>%
dplyr::mutate(freq = (n_2_grupos / sum(n_2_grupos)))%>%
dplyr::filter(fec_nacimiento_qrt>=min_plot) %>%
ggplot2::ggplot(aes(x = fec_nacimiento_qrt, y = freq,fill=grupo_var))+
geom_area(alpha=0.6 , size=.5, colour="white") +
scale_fill_manual(values= colorspace::rainbow_hcl(length(table(Base_fiscalia_v5[[i]])))) +
sjPlot::theme_sjplot2() +
scale_x_yearqtr(format="%YQ%q", n=18,
limits=c(zoo::as.yearqtr(paste0(min_plot,"-01-01")),
max=zoo::as.yearqtr(paste0(max_plot,"-01-01"))))+
scale_y_continuous(limits=c(0,1),labels = scales::percent)+
labs(x="",y="Percentages",
caption= paste0("Note. Cases born between ",min_plot," to ",max_plot,", but ignoring the rest: ", casos_no_cubiertos,
";\n",
"Percentages by year and quarter"))+
#ylim(0,101)+
# scale_y_continuous(limits=c(0,1),labels = scales::percent) +
theme(legend.position="bottom")+
guides(fill=guide_legend(ncol=3))+
theme(legend.title = NULL,
legend.text = element_text(size = 55),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank())+
theme(axis.text.x = element_text(vjust = 0.5,hjust = 0.5,angle = 60))+
theme(text=element_text(size=74))+
theme(plot.caption = element_text(hjust = 0, lineheight = .3))
Figure 8: Date of birth and suspicious of commiting a crime
Show code
NULLPreliminary Analyses for SER 2022 (part 2)
Show code
NULLShow code
tab1_lab_aft_d<- paste0('Original C1 Dataset \n(n = ', formatC(nrow(CONS_C1), format='f', big.mark=',', digits=0), ';\npatients: ',formatC(CONS_C1%>% dplyr::distinct(HASH_KEY)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab2_lab_aft_d<- paste0('•Remove duplicated entries\\\\\\l•Overlapping treatments of patients\\\\\\l•Intermediate treatment events (continuous referrals) \\\\\\l')
tab3_lab_aft_d<- paste0(' C1 Dataset \n(n = ', formatC(nrow(CONS_C1_df_dup_SEP_2020), format='f', big.mark=',', digits=0), ';\npatients: ',formatC(CONS_C1_df_dup_SEP_2020%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab4_lab_aft_d<- paste0('Original Prosecutors Office\n(n = ',format(nrow(Base_fiscalia_v2),big.mark=","),
';\nCauses= ',Base_fiscalia_v2%>% dplyr::distinct(ruc)%>% nrow() %>% format(big.mark=','),
';\nRel.=',Base_fiscalia_v2%>%dplyr::distinct(idrelacion)%>%nrow()%>%format(big.mark=','),
';\nRUC_Vic_Imp=',Base_fiscalia_v2%>%dplyr::mutate(rel=paste0(ruc,"_",idsujeto_victima,"_",idsujeto_imputado,"_","iddelito"))%>%dplyr::distinct(rel)%>%nrow()%>%format(big.mark=','),
';\nindividuals= ',Base_fiscalia_v2%>% dplyr::distinct(rut_enc_saf)%>% nrow() %>% format(big.mark=','),')')
tab5_lab_aft_d<- paste0('•Filter crimes committed after study follow-up period\\\\\\l•Remove duplicated entries\\\\\\l•Correct dates (birth, comission of crime, end of judicial proceedings) \\\\\\l•Define cases that acted as victims & imputed in a cause\\\\\\l')
tab6_lab_aft_d<- paste0("O.P. Dataset \n(n= ", formatC(nrow(Base_fiscalia_v5),big.mark = ","),";\nindividuals= ",Base_fiscalia_v5%>% dplyr::distinct(rut_enc_saf)%>% nrow()%>% formatC(big.mark = ","),")")
tab7_lab_aft_d<- paste0('•Long-to-wide relationships/crimes, end of judicial proceedings, penalty \\\\\\l•Group crimes into violent, drug-related, etc. \\\\\\l•Check overlapping events (e.g., incarcerated while completing a residential treatment) \\\\\\l')
library(DiagrammeR) #⋉
plot_merge_flowchart_after_dates<-
grViz([2506 chars quoted with '"'], width = 1200,
height = 900)
DPI = 1200
WidthCM = 11
HeightCM = 8
font_add(family = "Rooney Sans", regular = paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path),"/_style/RooneySansRegular.otf"))
showtext_begin()
plot_merge_flowchart_after_dates %>%
export_svg %>% charToRaw %>% rsvg_pdf(paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path),"/_figs/_flowchart_merge.pdf"))
plot_merge_flowchart_after_dates %>% export_svg()%>%charToRaw %>% rsvg(width = WidthCM *(DPI/2.54), height = HeightCM *(DPI/2.54)) %>% png::writePNG(paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path),"/_figs/_flowchart_merge_wo_fmt.png"))
htmlwidgets::saveWidget(plot_merge_flowchart_after_dates, paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path),"/_figs/_flowchart_merge_222.html"))
webshot(paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path),"/_figs/_flowchart_merge_222.html"), paste0(sub("2019 \\(github\\)/SUD_CL","2022 \\(github\\)",path),"/_figs/_flowchart_merge_formatted.png"),vwidth = 1200, vheight = 900,
zoom = 2)
Label
Show code
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
Base_fiscalia_v5%>%
dplyr::mutate_at(c('dg_trs_psiq_cie_10_or','x2_dg_trs_psiq_cie_10_or','x3_dg_trs_psiq_cie_10_or','x2_dg_trs_psiq_sub_cie_10_or','x3_dg_trs_psiq_sub_cie_10_or','compromiso_biopsicosocial','pais_nacimiento','nacionalidad','dg_trs_psiq_sub_dsm_iv_or','x2_dg_trs_psiq_sub_dsm_iv_or','x3_dg_trs_psiq_sub_dsm_iv_or','dg_trs_psiq_sub_cie_10_or','etnia_cor_2','motivodeegreso_mod_imp','fecha_ultimo_tratamiento','fecha_ultimo_tratamiento','tiene_menores_de_edad_a_cargo'),~as.factor(.)) %>%
dplyr::group_by(hash_key)%>%
dplyr::mutate(at_least_one_cont_entry=sum(!is.na(diff_bet_treat)))%>%
ungroup()%>%
dplyr::mutate(at_least_one_cont_entry= ifelse(at_least_one_cont_entry>0,1,0))%>%
dplyr::mutate(menor_45_dias_diff= ifelse(diff_bet_treat<45,1,0))%>%
dplyr::mutate(at_least_one_cont_entry= recode(as.character(at_least_one_cont_entry),"0"="User with no cont. entry","1"="User with cont. entry"))%>%
dplyr::mutate(menor_45_dias_diff= recode(as.character(menor_45_dias_diff),"0"=">= 45 Days of Difference Between Entries","1"="<45 Days of Difference Between Entries"))%>%
dplyr::mutate(menor_60_dias_diff= recode(as.character(menor_60_dias_diff),"0"=">= 60 Days of Difference Between Entries","1"="<60 Days of Difference Between Entries"))%>%
dplyr::mutate(obs_cambios_ninguno= recode(as.character(obs_cambios_ninguno),"0"="At least 1 Change w/ the Next Entry","1"="No Changes w/ the Next Entry"))%>%
dplyr::mutate(motivoegreso_derivacion= recode(as.character(motivoegreso_derivacion),"0"="Other causes of discharge","1"="Referral"))%>%
dplyr::mutate_at(vars(at_least_one_cont_entry,menor_45_dias_diff,menor_60_dias_diff,obs_cambios_ninguno,motivoegreso_derivacion,obs_cambios),~as.factor(.))%>%
assign("Base_fiscalia_v5",., envir = .GlobalEnv)
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
codebook::var_label(Base_fiscalia_v5) <- list(
row= 'Numerador de los eventos presentes en la Base de Datos/Events in the Dataset',
table= 'Origen de los Datos (de los archivos por año)/Source of Data (of files per year)',
hash_key= 'Codificación del RUN/Masked Identifier (RUN)',
ano_bd= 'Año de la Base de Datos/Year of the Dataset (Source)',
id= 'Codigo Identificación de SENDA/SENDA ID',
nombre_centro= 'Nombre del Centro de Tratamiento/Treatment Center',
tipo_centro= 'Tipo de Centro/Type of Center',
region_del_centro= '(original, Recodificado en nombre_region)/',
servicio_de_salud= 'Servicio de Salud/Health Service',
tipo_de_programa= '(original, Recodificado en tipo_de_programa_2)/',
tipo_de_plan= '(original, Recodificado en tipo_de_plan_2)/',
senda= 'SENDA/SENDA',
dias_trat= 'Días de Tratamiento/Days of Treatment',
nmesesentratamiento= 'Número de Meses en Tratamiento/Number of Months in Treatment',
dias_en_senda= 'Días en SENDA/Days in SENDA',
n_meses_en_senda= 'Número de Meses en SENDA/Number of Months in SENDA',
sexo= '(original, Recodificado en sexo_2)/',
edad= 'Edad (número entero)/Age (In years, Discrete Number)',
nombre_usuario= 'Nombre del Usuario (OCULTO y no accesible)/Name of the User (Not Accessible)',
comuna_residencia= '(original, Recodificado en comuna_residencia_cod)/',
origen_de_ingreso= '(original, Recodificado en origen_ingreso)/',
pais_nacimiento= 'País de Nacimiento/Country of Birth',
nacionalidad= 'Nacionalidad/Nationality',
etnia= '(original, recodificado en etnia_cor)/',
estado_conyugal= '(original, Recodificado en estado_conyugal_2)/',
numero_de_hijos= 'Número de Hijos/Number of Children',
num_hijos_ing_trat_res= 'Número de Hijos para Ingreso a Tratamiento Residencial/Number of Children to Residential Treatment',
parentesco_con_el_jefe_de_hogar= '(Sólo presenta valores perdidos)/',
num_trat_ant= 'Número de Tratamientos Anteriores/Number of Previous Treatments',
fecha_ultimo_tratamiento= 'Fecha del Último Tratamiento/Date of the Last Treatment',
sustancia_de_inicio= '(original, Recodificado en sus_ini)/',
edad_inicio_consumo= '(original, Recodificado en edad_ini_cons)/',
x_se_trata_mujer_emb= 'Mujer Embarazada al Ingreso/Pregnant at Admission',
escolaridad_ultimo_ano_cursado= '(original, Recodificado en escolaridad)/',
condicion_ocupacional= '(original, Recodificado en estatus_ocupacional)/',
categoria_ocupacional= '(original, Recodificado en cat_ocupacional)/',
rubro_trabaja= '(original, Recodificado en rubro_trabaja_mod)/',
con_quien_vive= 'Persona con la que vive el Usuario/People that Share Household with the User',
tipo_de_vivienda= '(original, Recodificado en tipo_de_vivienda_mod)/',
tenencia_de_la_vivienda= '(original, Recodificado en tenencia_de_la_vivienda_mod)/',
sustancia_principal= '(original, Recodificado en sus_principal)/',
`otras_sustancias_nº1`= '(original, Recodificado en otras_sus1)/',
`otras_sustancias_nº2`= '(original, Recodificado en otras_sus2)/',
`otras_sustancias_nº3`= '(original, Recodificado en otras_sus3)/',
freq_cons_sus_prin_original= '(original, Recodificado en freq_cons_sus_prin)/',
edad_inicio_sustancia_principal= '(original, Recodificado en edad_ini_sus_prin)/',
via_adm_sus_prin_original= '(original, Recodificado en via_adm_sus_prin)/',
dg_trs_cons_sus_or= 'Diagnósico de Trastorno por Consumo de Sustancias/Diagnosed of Substance Use Disorder',
dg_trs_psiq_dsm_iv_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios DSM IV/Diagnosis of Psychiatric Disorders, DSM-IV criteria',
dg_trs_psiq_sub_dsm_iv_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios DSM IV (Subclasificacion)/Diagnosis of Psychiatric Disorders, DSM-IV criteria (sub-classification)',
x2_dg_trs_psiq_dsm_iv_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios DSM IV (2)/Diagnosis of Psychiatric Disorders, DSM-IV criteria (2)',
x2_dg_trs_psiq_sub_dsm_iv_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios DSM IV (Subclasificacion) (2)/Diagnosis of Psychiatric Disorders, DSM-IV criteria (sub-classification) (2)',
x3_dg_trs_psiq_dsm_iv_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios DSM IV (3)/Diagnosis of Psychiatric Disorders, DSM-IV criteria (3)',
x3_dg_trs_psiq_sub_dsm_iv_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios DSM IV (Subclasificacion) (3)/Diagnosis of Psychiatric Disorders, DSM-IV criteria (sub-classification) (3)',
dg_trs_psiq_cie_10_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios CIE-10/Diagnosis of Psychiatric Disorders, CIE-10 criteria',
dg_trs_psiq_sub_cie_10_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios CIE-10 (Subclasificacion)/Diagnosis of Psychiatric Disorders, CIE-10 criteria (subclassification)',
x2_dg_trs_psiq_cie_10_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios CIE-10 (2)/Diagnosis of Psychiatric Disorders, CIE-10 criteria (2)',
x2_dg_trs_psiq_sub_cie_10_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios CIE-10 (Subclasificacion) (2)/Diagnosis of Psychiatric Disorders, CIE-10 criteria (subclassification) (2)',
x3_dg_trs_psiq_cie_10_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios CIE-10 (3)/Diagnosis of Psychiatric Disorders, CIE-10 criteria (3)',
x3_dg_trs_psiq_sub_cie_10_or= 'Diagnóstico de Trastorno Psiquiátrico, Criterios CIE-10 (Subclasificacion) (3)/Diagnosis of Psychiatric Disorders, CIE-10 criteria (subclassification) (3)',
diagnostico_trs_fisico= 'Diagnóstico de Trastorno Físico/Diagnosis of Physical Disorder',
otros_probl_at_sm_or= 'Otros Problemas de Atención Vinculados a Salud Mental/Other problems linked to Mental Health',
compromiso_biopsicosocial= 'Compromiso Biopsicosocial/Biopsychosocial Involvement',
dg_global_nec_int_soc_or= 'Diagnóstico Global de Necesidades de Integración Social (Al Ingreso)/Global Diagnosis of Social Integration (At Admission)',
dg_nec_int_soc_cap_hum_or= 'Diagnóstico de Necesidades de Integración Social en Capital Humano (Al Ingreso)/Global Diagnosis of Social Integration in Human Capital (At Admission)',
dg_nec_int_soc_cap_fis_or= 'Diagnóstico de Necesidades de Integración Social en Capital Físico (Al Ingreso)/Global Diagnosis of Social Integration in Physical Capital (At Admission)',
dg_nec_int_soc_cap_soc_or= 'Diagnóstico de Necesidades de Integración Social en Capital Social (Al Ingreso)/Global Diagnosis of Social Integration in Social Capital (At Admission)',
fech_ing= 'Fecha de Ingreso a Tratamiento/Date of Admission to Treatment',
fecha_ingreso_a_convenio_senda= 'Fecha de Ingreso a Convenio SENDA (aún no formateada como fecha)/Date of Admission to SENDA Agreement',
usuario_tribunal_trat_droga= 'Usuario de modalidad Tribunales de Tratamiento de Drogas/User of Drug Treatment Courts Modality',
consentimiento_informado= 'Consentimiento Informado/Informed Consent',
fech_egres= 'Fecha de Egreso de Tratamiento/Date of Discharge from Treatment',
motivodeegreso= 'Motivo de Egreso/Cause of Discharge',
tipo_centro_derivacion= 'Tipo de Centro al que el Usuario es Derivado/Type of Center of Derivation',
evaluacindelprocesoteraputico= 'Evaluación del Proceso Terapéutico/Evaluation of the Therapeutic Process',
eva_consumo= 'Evaluación al Egreso Respecto al Patrón de consumo/Evaluation at Discharge regarding to Consumption Pattern',
eva_fam= 'Evaluación al Egreso Respecto a Situación Familiar/Evaluation at Discharge regarding to Family Situation',
eva_relinterp= 'Evaluación al Egreso Respecto a Relaciones Interpersonales/Evaluation at Discharge regarding to Interpersonal Relations',
eva_ocupacion= 'Evaluación al Egreso Respecto a Situación Ocupacional/Evaluation at Discharge regarding to Occupational Status',
eva_sm= 'Evaluación al Egreso Respecto a Salud Mental/Evaluation at Discharge regarding to Mental Health',
eva_fisica= 'Evaluación al Egreso Respecto a Salud Física/Evaluation at Discharge regarding to Physical Health',
eva_transgnorma= 'Evaluación al Egreso Respecto a Trasgresión a la Norma Social/Evaluation at Discharge regarding to Transgression to the Norm',
dg_trs_psiq_cie_10_egres_or= '(Sólo presenta valores perdidos)/',
dg_global_nec_int_soc_or_1= 'Diagnóstico Global de Necesidades de Integración Social (Al Egreso)/Global Diagnosis of Social Integration (At Discharge)',
dg_nec_int_soc_cap_hum_or_1= 'Diagnóstico de Necesidades de Integración Social en Capital Humano (Al Egreso)/Global Diagnosis of Social Integration in Human Capital (At Discharge)',
dg_nec_int_soc_cap_fis_or_1= 'Diagnóstico de Necesidades de Integración Social en Capital Físico (Al Egreso)/Global Diagnosis of Social Integration in Physical Capital (At Discharge)',
dg_nec_int_soc_cap_soc_or_1= 'Diagnóstico de Necesidades de Integración Social en Capital Social (Al Egreso)/Global Diagnosis of Social Integration in Social Capital (At Discharge)',
tiene_menores_de_edad_a_cargo= 'Menores de Edad A Cargo/Minor Dependants',
mot_egres_alt_adm_or= 'Motivo de Egreso Alta Administrativa/Cause of Administrative Discharge',
consorcio= 'Sociedades de Tratamiento Servicios de Salud- Fundaciones- entre otras entidades encargadas de los centros/Consortium',
id_centro= 'ID de Centro/Treatment center ID',
ha_estado_embarazada_egreso= '¿Ha estado embarazada? (al Egreso)/Have you been Pregnant (at Discharge)',
identidad_de_genero= 'Identidad de Género/Gender Identity',
discapacidad= 'Presenta Discapacidad/Disability',
hash_rut_completo= 'HASH alternativo, en el escenario en que se asuma que el individuo al que se le codificó el RUN presente mayor edad/Alternative HASH-Key',
opcion_discapacidad= 'Origen de Discapacidad/Cause of Disability',
sexo_2= 'Sexo Usuario/Sex of User',
embarazo= 'Embarazo al Ingreso /Pregnant at Admission',
tipo_de_plan_2= 'Tipo de Plan/Type of Plan',
tipo_de_programa_2= 'Tipo de Programa de Tratamiento/Type of Program',
fech_egres_sin_fmt= 'Fecha de Egreso de Tratamiento (Sin Formato de Fecha)/Date of Discharge',
id_mod= 'ID de SENDA para Presentación en Página Web (enmascara caracteres 5 y 6)/SENDA ID (mask characters 5 & 6)',
ano_nac= 'Año de Nacimiento (numérico)/Year of Birth (numeric)',
fech_ing_ano= 'Año de Ingreso (numérico)/Year of Admission (numeric)',
fech_ing_mes= 'Mes de Ingreso (numérico)/Month of Admission (numeric)',
fech_ing_dia= 'Día de Ingreso (numérico)/Day of Admission (numeric)',
concat= 'ID de SENDA y HASH Concatenado (permite discriminar más de un HASH en un mismo ID)/Combination of SENDA ID & HASH',
obs= 'Observaciones al Proceso de Limpieza y Estandarización de Casos/Observations to the Process of Data Tidying & Standardization',
dias_trat_inv= 'Días de Tratamiento Invertidos (fecha más reciente, menor valor numérico)/Treatment Days (Reversed)',
fech_nac= 'Fecha de Nacimiento/Date of Birth',
edad_al_ing= 'Edad a la Fecha de Ingreso a Tratamiento (numérico continuo)/Age at Admission to Treatment',
edad_ini_cons= 'Edad de Inicio de Consumo/Age of the Onset of Drug Use',
edad_ini_sus_prin= 'Edad de Inicio de Consumo Sustancia Principal/Age of the Onset of Drug Use of Primary Substance',
dias_trat_alta_temprana= 'Días de tratamiento (<90)/Less than 90 days in treatment',
motivodeegreso_mod= 'Motivo de Egreso (con abandono temprano y tardío)/Cause of Discharge (with late and early withdrawal)',
sus_principal= 'Sustancia Principal de Consumo/Primary or Main Substance of Consumption at Admission',
otras_sus1= 'Otras Sustancias (1)/Other Substances (1)',
otras_sus2= 'Otras Sustancias (2)/Other Substances (2)',
otras_sus3= 'Otras Sustancias (3)/Other Substances (3)',
sus_ini= 'Sustancia de Inicio/Starting Substance',
estado_conyugal_2= 'Estado Conyugal/Marital Status',
estatus_ocupacional= 'Condición Ocupacional/Occupational Status',
cat_ocupacional= 'Categoría Ocupacional/Occupational Category',
edad_grupos= 'Edad agrupada/Age in groups',
origen_ingreso= "(modificado en origen_ingreso_mod)/",
escolaridad= 'Escolaridad: Nivel Eduacional/Educational Attainment',
via_adm_sus_prin= 'Vía de Administración de la Sustancia Principal/Route of Administration of the Primary or Main Substance',
freq_cons_sus_prin= 'Frecuencia de Consumo de la Sustancia Principal (30 días previos a la admisión)/Frequency of Consumption of the Primary or Main Substance (30 days previous to admission)',
dias_trat_knn_imp= 'Días de Tratamiento (Imputados KNN)/Days of Treatment (Imputed KNN)',
fech_egres_knn_imp= 'Fecha de Egreso (Imputados KNN)/Date of Discharge (Imputed KNN)',
dias_trat_alta_temprana_knn_imp= 'Días de Tratamiento con Alta Temprana (<90) (Imputados KNN)/Days of Treatment w Early Withdrawal (Imputed KNN)',
fech_egres_imp= 'Fecha de Egreso (Imputados KNN & Lógico)/Date of Discharge (Imputed KNN & Logic)',
motivodeegreso_imp= 'Motivo de Egreso(Imputados KNN & Lógico)/Cause of Discharge (Imputed KNN & Logic)',
motivodeegreso_mod_imp= 'Motivo de Egreso (con abandono temprano y tardío)(Imputados KNN & Lógico)/Cause of Discharge (with late and early withdrawal)(Imputed KNN & Logic)',
dias_trat_imp= 'Días de Tratamiento (Imputados KNN & Lógico)/Days of Treatment (Imputed KNN & Logic)',
dias_trat_alta_temprana_imp= 'Días de Tratamiento con Alta Temprana (<90) (Imputados KNN & Lógico)/Days of Treatment w Early Withdrawal (Imputed KNN & Logic)',
via_adm_sus_prin_act= 'Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)/Route of Administration of the Primary or Main Substance (Tidy)',
etnia_cor= 'Etnia/Ethnic Group',
nacionalidad_2= 'Segunda Nacionalidad/Second Nationality',
etnia_cor_2= 'Etnia (2)/Second Ethnic Group',
sus_ini_2= 'Segunda Sustancia de Inicio/Second Starting Substance',
sus_ini_3= 'Tercera Sustancia de Inicio/Third Starting Substance',
concat_hash_sus_prin= 'Combination of User & Primary Substance',
macrozona= "Macrozona/Macrozones",
nombre_region= " Región del Centro/Chilean Region of the Center",
comuna_residencia_cod= "Comuna de Residencia/Municipality or District of Residence",
sus_ini_mod= "Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent)",
sus_principal_mod= 'Sustancia Principal de Consumo (Sólo más frecuentes)/Primary or Main Substance of Consumption at Admission (Only more frequent)',
origen_ingreso_mod= 'Origen de Ingreso/Motive of Admission to Treatment',
tipo_de_vivienda_mod= 'Tipo de Vivienda/Type of Housing',
tenencia_de_la_vivienda_mod= 'Tenencia de la Vivienda/Tenure status of Households',
rubro_trabaja_mod= 'Rubro de Trabajo/Area of Work',
edad_al_ing_grupos= 'Edad a la Fecha de Ingreso a Tratamiento en Grupos/Age at Admission to Treatment In Groups',
menor_60_dias_diff= 'Menor a 60 días de diferencia con el registro posterior/Menor a 60 days of difference between the next entry',
menor_45_dias_diff= 'Menor a 45 días de diferencia con el registro posterior/Less than 45 days of difference between the next entry',
diff_bet_treat= 'Días de diferencia con el registro posterior/Days of difference between the next entry',
id_centro_sig_trat= "ID del Centro del registro posterior/Center ID of the Next Treatment",
tipo_plan_sig_trat= "Tipo de Plan del registro posterior/Type of Plan of the Next Entry",
tipo_programa_sig_trat= "Tipo de Programa del registro posterior/Type of Program of the Next Entry",
senda_sig_trat= "SENDA del registro posterior/SENDA of the Next Entry",
motivoegreso_derivacion= "Motivo de Egreso= Derivación/Cause of Discharge= Derivación",
obs_cambios= "Cambios del tratamiento en comparación al registro posterior/Changes in treatment compared to the Next Entry",
obs_cambios_ninguno= "Sin cambios del tratamiento en comparación al registro posterior/No changes in treatment compared to the Next Entry",
obs_cambios_num= "Recuento de cambios del tratamiento en comparación al registro posterior/Count of changes in treatment compared to the Next Entry",
obs_cambios_fac= "Recuento de cambios del tratamiento en comparación al registro posterior(factor)/Count of changes in treatment compared to the Next Entry(factor)",
at_least_one_cont_entry= "Casos de Usuarios con más de una entrada después de otra/Cases of users with more than one entry after another one"
)
Session Info
Show code
cat(Sys.getenv("R_LIBS_USER"), fill=T)
C:/Users/CISS Fondecyt/Mi unidad/Alvacast/SISTRAT 2022 (github)/renv/library/R-4.1/x86_64-w64-mingw32;C:/Program Files/R/R-4.1.2/libraryShow code
Sys.Date()
[1] "2022-08-10"Editor context: C:/Users/CISS Fondecyt/Mi unidad/Alvacast/SISTRAT 2022 (github)Show code
if (grepl("CISS Fondecyt",rstudioapi::getSourceEditorContext()$path)==T){
save.image("C:/Users/CISS Fondecyt/Mi unidad/Alvacast/SISTRAT 2022 (github)/11.RData")
} else if (grepl("andre",rstudioapi::getSourceEditorContext()$path)==T){
save.image("C:/Users/andre/Desktop/SUD_CL/11.RData")
} else if (grepl("E:",rstudioapi::getSourceEditorContext()$path)==T){
save.image("E:/Mi unidad/Alvacast/SISTRAT 2022 (github)/11.RData")
} else {
save.image(paste0(sub("2019","2022",sub("SUD_CL","",path)),"11.RData"))
}
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({'font-size': '80%'});",
"}")))
Export
Show code
Base_fiscalia_v5 %>%
dplyr::mutate(accused=dplyr::case_when(encontrado_como_victima=="SI" & encontrado_como_imputado=="SI"~"IMPUTADO",
encontrado_como_victima=="NO" & encontrado_como_imputado=="SI"~"IMPUTADO",
T~"VICTIMA")) %>%
#dplyr::filter(accused=="VICTIMA") %>%
rio::export(file = paste0("fiscalia_mariel_jun_2022.dta"))
# ```{r label_to_stata, echo=T, paged.print=TRUE}
#
# export_lab_stata_merge<-
# tibble::rownames_to_column(data.frame(Hmisc::label(Base_fiscalia_v2)))%>% data.frame() %>%
# dplyr::rename("code" = !!names(.[1]), "label" = !!names(.[2]))%>% data.frame()%>%
# dplyr::mutate(first= "cap noi label variable")%>%
# dplyr::mutate(final= paste0(first, " ",code,' "',label,'"'))%>%
# dplyr::select(-code,-label,-first)%>%
# dplyr::rename("*clear all"="final") %>%
# rbind(paste0('cap noi save "', gsub('/', '\\', path, fixed=T),'\\fiscalia_mariel_ago_2022.dta", replace'))%>%
# rbind('cap noi drop id id_mod nombre_centro consentimiento_informado')%>%
# rbind('cap noi drop id id_mod nombre_centro')%>%
# rbind(paste0('cap noi save "', gsub('/', '\\', path, fixed=T),'\\fiscalia_mariel_ago_2022.dta", replace'))
#
# rbind(paste0('cap noi use "', gsub('/', '\\', path, fixed=T),'\\fiscalia_mariel_ago_2022.dta", clear'),export_lab_stata_merge) %>% knitr::kable("html") %>%
# kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size =10) %>%
# kableExtra::scroll_box(width = "100%", height = "375px")
#
# write.table(rbind(paste0('cap noi use "', gsub('/', '\\', path, fixed=T),'\\fiscalia_mariel_ago_2022.dta", clear'),export_lab_stata_merge), file = paste0(path,"/SUD_CL/_label_var_to_stata.do"), sep = "",row.names = FALSE, quote = FALSE, fileEncoding="UTF-8")
# ```
#
# <br>
#
# <div style="border: 1px solid #ddd; padding: 5px; overflow-y: scroll; height:350px; overflow-x: scroll; width:100%">
#
# ```{stata 2, collectcode=F, include=T, error=T, cleanlog=F}
# *should be in the same folder of the .Rmd to work
# cap noi do _label_var_to_stata.do
# ```
#
# </div>